By Kyle, Investment Manager@Bing Ventures
Users enjoy personalised services provided by big data, which brings great convenience to life, but the information collected includes sensitive information such as identity, hobbies, geographical location, and personal income. Once this personal privacy information is leaked, it will bring great security risks. Therefore, the emergence of privacy computing technology provides a solution for user data security.
Privacy Computing refers to a comprehensive technology that realises the separation of data privacy and usability through confidential technologies such as federated learning, secure multi-party, homomorphic encryption, and zero-knowledge proof. In addition to protecting data privacy, it enables data owners to control their own data and obtain valuable algorithmic technology from it.
Under the privacy computing framework, the plaintext data of the participating parties does not leave the local area, so as to protect data security and realise multi-source data cross-domain cooperation, which can effectively solve the problems of data protection and fusion applications. To put it simply, privacy computing realizes “data is available but not visible” and the efficient transformation and circulation of data value on the premise of ensuring that the data itself is not leaked to the outside world.
Privacy computing can help users realise remote computing functions without explicitly disclosing their data. In recent years, the privacy computing industry has grown rapidly. According to the latest data, as of 2020, the global privacy computing market has reached 28.61 billion US dollars, and it is expected that by 2026, the market size will reach 111.89 billion US dollars, with a growth rate of 28.6%.
As the industry has evolved, so have the latest demands in the privacy computing space. Privacy computing also requires powerful computing power to meet users’ requirements for higher efficiency and quality. In addition, trusted computing is also an emerging field in privacy computing. Users can implement trusted computing without exposing data.
Source: SciencDirect
The application fields of privacy computing are also expanding. Traditional application fields are mainly concentrated in the fields of finance, medical care, government, and law. However, with the development of technology, privacy computing has entered new fields.
The development of privacy computing has also promoted the development of related technologies. For example, in privacy computing, cryptography plays an important role. With the development of privacy computing, encryption technology has also made great progress, such as encryption technology based on random numbers and error-correcting codes.
To sum up, the privacy computing industry is developing rapidly, and its core requirements include: (1) users’ needs for privacy protection and security; (2) more powerful computing capabilities to meet users’ requirements; (3) trustworthy computing development. In the future, the privacy computing industry will continue to develop and grow, and continue to meet users’ needs for privacy protection, security, and efficiency.
There are many ways to implement privacy computing technology. The common implementation methods of privacy computing technology include:
1. Encrypted domain computing: By encrypting data and operations, it ensures that the true content of the data will not be revealed during the operation.
2. Anonymous computing: By obfuscating the true source identity of the data, it ensures that the true content of the data will not be revealed during the calculation.
3. Obfuscated computing: By obfuscating the data, it ensures that the true content of the data will not be revealed during the calculation.
Among them, encrypted computing is the most commonly used method. It uses the principle of cryptography to protect the privacy of data by encrypting data. Anonymous computing is another way of privacy protection, which obfuscates user identities during data processing to prevent user privacy from being leaked. Obfuscated computing is a special anonymous computing method, which uses probability theory to add noise in the data processing process, making the result uncertain, thereby protecting user privacy.
From the comparative analysis of advantages and disadvantages, both encrypted domain computing and anonymous computing have certain security. But in the case of complex calculations, the performance of encrypted domain calculations is poor, and the security of anonymous calculations is not particularly high. In contrast, the performance and security of confusing computing are better, but the implementation is more complicated and not suitable for some simple operations.
The realisation of privacy computing technology is inseparable from the following three types of technologies: encryption technology, privacy protection mechanism and enhanced security technology.
Source: Gartner
Encryption is the most commonly used technique in privacy computing. The principle is to use key encryption technology to encrypt the data of each participant, so that the data can only be safely transmitted between participants. The advantage of encryption technology is that it can protect the integrity of data and ensure data security. Its disadvantage is that the confidentiality of the data cannot be guaranteed, since the encrypted data is still visible.
The privacy protection mechanism is also a commonly used privacy computing technology, which uses technologies such as digital signatures and message authentication codes to ensure the confidentiality of data transmission between participants and the integrity of messages. The advantage of the privacy protection mechanism is that it can protect the data security between the participants and ensure the authenticity and integrity of the message. Its disadvantage is that since the data has been encrypted, the computational efficiency between the participating parties will be affected to a certain extent.
Enhanced security technology is a technology for complex privacy computing scenarios. It uses technologies such as identity authentication and expiration date management to protect the data security between participants to ensure that data will not be leaked during transmission. The advantage of hardened security technology is that it can improve the security of data transmission between participating parties. Its disadvantage is that due to the double encryption, the calculation efficiency will be affected to a certain extent.
Through the above analysis, it can be seen that encryption technology and privacy protection mechanism can effectively protect the data security between participants, while enhanced security technology can better protect the data security between participants, but it will affect the computing efficiency. certain influence. Therefore, in different privacy computing scenarios, it is necessary to select the appropriate privacy computing technology according to the actual situation to ensure data security and computing efficiency.
From the perspective of technology classification, privacy computing has the following main directions: first, the privacy computing technology stack with cryptography as the core, including secure multi-party computing, zero-knowledge proof, and homomorphic encryption; Information execution environment and differential privacy and other technologies. We focus on the following mainstream technical solutions, namely: secure multi-party computation (MPC), zero-knowledge proof (ZKP), homomorphic encryption (HE), trusted execution environment (TEE), and federated learning (FL).
In the absence of participants sharing their own data and without a trusted third party, this technology can still perform collaborative computing, ultimately producing valuable analytical content.
Advantage:
Based on the security principle of cryptography, its security is proved by strict cryptography theory, and it is not based on trusting any participant, operator, system, hardware or software. Each participant has absolute control over the data it owns, guarantees that basic data and information will not be leaked, and at the same time has high calculation accuracy and supports programmable general-purpose computing.
Shortcoming:
Multi-party secure computing involves complex cryptographic operations, and computing performance is a major obstacle to its application. With the expansion of the application scale, it is a major challenge for manufacturers to adopt a suitable computing solution to ensure that the computing time and the number of participants show a linear change. From the perspective of security, the goal of multi-party secure computing is to ensure the privacy and security of multi-party data fusion computing. Some traditional security issues, such as access control and transmission security, still need other corresponding technical means to support.
The prover can convince the verifier of the authenticity of the data without revealing the specific data. Zero-knowledge proof can be interactive, that is, the prover has to prove the authenticity of the data once to each verifier; it can also be non-interactive, that is, the prover creates a proof, and anyone who uses this proof can be verified.
Advantage:
The main value of zero-knowledge proof lies in the use of private data sets on transparent public chains such as Ethereum. Blockchain combined with zero-knowledge proof technology allows users and businesses to use private data to execute smart contracts without disclosing specific data content. Zero-knowledge proofs are currently most widely used in blockchains for private transactions. ZCash has already started using zero-knowledge proofs, which hide transaction amounts and sender and receiver addresses.
Shortcoming:
Need to find a trusted third party to create initialisation parameters. If the random numbers used in the creation process are not continuously deleted, then the person who obtained these random numbers can successfully forge the proof. This is the so-called trust setup problem.
Homomorphic encryption is a form of encryption that allows people to perform specific forms of algebraic operations on ciphertext to obtain an encrypted result, and the result obtained by decrypting it is the same as the result of the same operation on plaintext.
Advantage:
This technology allows people to process encrypted data to obtain correct results without decrypting the data during the entire process. Fully homomorphic encryption systems are generally based on basic tools such as lattice theory, which can resist quantum attacks.
Shortcoming:
A core problem that homomorphic encryption needs to solve is that it can support any type of calculation. Arbitrary means addition and multiplication, because all calculation programs abstracted as arithmetic circuit representations can be realised by addition and multiplication.
It has computing and storage functions, and can provide a separate processing environment with security and integrity protection. Programs and data in this environment can be protected at a higher level than the operating system level (OS).
Advantage:
Confidential computing in a trusted execution environment has the advantages of being versatile and efficient, and can seamlessly support general computing frameworks and applications, while computing performance is basically comparable to plaintext computing. It can be used for privacy computing alone or combined with other techniques to preserve privacy.
Shortcoming:
The disadvantage of confidential computing is that the TEE trust chain is bound to CPU manufacturers. At present, hardware technology is in the hands of a few core suppliers such as Intel, Qualcomm, and ARM, which affects the credibility of confidential computing technology. Another disadvantage of confidential computing is that current TEE implementations theoretically have the possibility of side-channel attacks, because TEE shares a large amount of system resources with other untrusted execution environment spaces.
This is a distributed machine learning technique or framework originally proposed by Google. It realizes multi-party joint learning and training through the circulation and processing of intermediate encrypted data without the local original data leaving the database. At present, federated learning technology is usually combined with secure multi-party computing technology and blockchain technology.
Advantage:
It can solve the problem of single data features in the training phase, so as to obtain a model with better performance than that trained by using its own data set.
Shortcoming:
It has security problems and communication efficiency problems. Because building a basic neural network model from the underlying code is usually time-consuming and labor-intensive, most companies currently obtain basic models from open source platforms or purchase basic models from third-party platforms. Such basic models themselves may be implanted with viruses. At the same time, the academic community has not yet strictly defined the security effect of federated learning.
It is still possible to deduce the data information of each participant by using the gradient and weight information collected by the central server. In addition, the federated learning mechanism assumes that all participants are trusted parties, and there is no way to prevent a participant from maliciously providing false data or even diseased data, which will cause irreversible damage to the final training model.
In today’s society, data security and privacy protection have become indispensable demands, and privacy computing has become one of the best solutions for data security and privacy protection. The industry value of privacy computing technology is mainly reflected in the following aspects:
**1. Improve data protection and privacy protection capabilities. **Privacy computing technology can protect data privacy and security, avoid data leakage and abuse.
**2. Improve the accuracy and efficiency of data analysis and machine learning. **Privacy computing technology can improve the accuracy and efficiency of data analysis and machine learning, make models more accurate and improve the accuracy of decisions.
**3. Promote the development of big data analysis and machine learning. **Privacy computing technology can promote the development of big data analysis and machine learning, support more data analysis and machine learning applications, and expand application scenarios.
4. Meet the needs of the government and society for data security and privacy protection.
Specifically, firstly, private computing technology can help enterprises improve the efficiency of data analysis. Traditional data analysis methods need to store data together, which increases the risk of data security. Privacy computing technology can carry out data analysis without moving the original data, avoiding data loss and leakage. In this way, enterprises can conduct data analysis more quickly, and save a lot of time and cost.
Secondly, private computing technology can help enterprises improve the accuracy of data analysis. Centralized data storage will lead to data confusion, resulting in inaccurate analysis results. The privacy computing technology can analyze the data without damaging the privacy of the original data, making the analysis results more accurate. In this way, enterprises can rely on accurate data analysis results to make more effective business decisions.
Source: Nature
Privacy computing may be used in the following application scenarios:
**1. Medical data analysis: **Improve the quality of medical services through data analysis, while ensuring the privacy of patients. Privacy computing technology can be used for data analysis under the premise of ensuring patient privacy.
**2. Social media analysis: **Learn users’ preferences through data analysis, and make recommendations and advertising. Privacy computing technology can be used to analyze data under the premise of ensuring user privacy.
**3. Financial data analysis: **improve the risk management ability of financial institutions through data analysis, while protecting customer privacy. Privacy computing technology can be adopted to ensure the privacy of customers under the premise of data analysis.
**4. Consumer behavior analysis: **understand consumers’ needs and preferences through data analysis, and conduct product marketing and pricing strategies. Privacy computing technology can be used to analyze data under the premise of ensuring consumer privacy.
Privacy computing has a wide range of applications in the blockchain industry.
**1. Financial services: **Privacy computing can be used for user authentication, transaction records, etc.
**2. Smart contracts: **Privacy computing can be used to develop smart contracts.
**3. Social media: **Privacy computing can be used for user authentication, data analysis and storage in social media.
**4. Supply chain: **Privacy computing can be used for data storage, trajectory tracking and warehousing in the supply chain.
**5. Data analysis: **Privacy computing can be used for data analysis to ensure data security and privacy protection.
**6. Healthcare: **Privacy computing can be used for data storage, trajectory tracking, and warehousing in healthcare.
**7. Digital identity: **Privacy computing can be used for the development of digital identities.
These are the detailed application scenarios of privacy computing in the blockchain industry. In short, privacy computing is an important technology. It can provide better data analysis capabilities for a wide range of industries while protecting data privacy.
Privacy computing can not only ensure the security and privacy of data interaction between various organizations in the industry, but also eliminate the concerns of individuals or organisations about data security, so as to obtain more data support.
There are three main bottlenecks in the development of privacy computing:
**1. Technical difficulties: **Privacy computing requires the combination of data analysis and privacy protection, which requires a series of new algorithms and technologies to achieve. At present, this aspect of technology is not fully mature, and still needs to continue to be explored and improved.
**2. Lack of standards: **As an emerging field, privacy computing lacks unified standards and specifications, which makes it difficult for different technical solutions to be compatible and interoperable. This hinders the popularity and application of private computing.
**3. Trust problem: **Privacy computing involves sensitive personal privacy. If there is no sufficient trust mechanism, it is difficult to gain the trust and acceptance of users. At present, there is no perfect trust system in the field of privacy computing, which requires in-depth research in the aspects of technology and management.
Privacy computing technology currently faces several challenges:
**1. Balance between data protection and privacy protection. **With the development of big data analysis technology, data analysis often involves a large amount of sensitive personal information. Hence, how to protect the privacy of users while ensuring the accuracy of analysis has become an important issue.
**2. Security of privacy computing. **Privacy computing requires the use of distributed computing technology. Thus, ensuring that data is not tampered or leaked in the process of distributed computing becomes a technical problem.
**3. Scalability of privacy computing. **With the increase of data volume, it is a challenge for privacy computing systems to ensure the scalability of analysis while maintaining privacy.
**4. The realization of privacy computing. **Privacy computing systems need to be deeply studied in algorithms, architecture and implementation, and how to achieve efficient and accurate privacy computing has become a technical problem.
At present, there are several missing problems in the field of privacy computing, such as:
**1. Reliability problems. **Privacy computing should ensure the accuracy and reliability of data as well as the privacy of data. But at present, many privacy computing technologies have reliability problems, such as the attack and cracking of encryption algorithms, the robustness of privacy obfuscation algorithms and so on.
**2. Scalability problem. **Privacy computing technology needs to support large-scale data processing, but many privacy computing methods have scalability problems, such as high computational complexity and performance bottlenecks.
**3. Verifiability issues. **Privacy computation needs to verify the data privacy protection mechanism, but many privacy computation methods currently have the problem of verifiability, such as the failure to verify whether the data is tampered with, whether the privacy protection mechanism is feasible, etc.
**4. Storage problems. **On the one hand, with the continuous development and application of data analysis technology, the way of data storage, analysis and utilisation is also constantly changing. In this context, mechanisms to protect the privacy of personal data need to evolve to adapt to different ways of storing, analysing and exploiting data. However, at present, due to the lack of coordination between different departments of the data privacy protection mechanism, the progress of the update and improvement of the privacy protection mechanism is slow, which cannot timely adapt to the development of data analysis technology.
On the other hand, with the development of data analysis technology, the storage, analysis and utilisation of data are becoming increasingly diversified. In this context, mechanisms to protect the privacy of personal data need to cover many different ways of storing, analysing and exploiting data. However, at present, due to the lack of coordination between different departments of the data privacy protection mechanism, there are overlapping and contradictory privacy protection mechanisms in various departments, which makes the implementation and execution of the privacy protection mechanism chaotic.
At present, there is a problem of trust in privacy computing, because in the process of data analysis, the data needs to be comprehensively analyzed, and often the data of multiple parties needs to be jointly analysed, which requires data privacy issues. For example, in the banking industry, banks usually need to cooperate with multiple parties to analyse customer credit information. However, if the sensitive data of customers is directly exposed to external partners, the risk of privacy disclosure will be brought.
In order to solve this trust problem, some solutions have been proposed, such as blockchain technology, encryption technology, anonymous technology and so on. These technologies can effectively protect data privacy, but they also have some limitations. For example, the performance of blockchain technology is limited and cannot cope with the demands of large-scale data analysis; Encryption technology may affect data analysis and affect the accuracy of data analysis.
Therefore, to solve the trust problem of privacy computing, it is necessary to improve the accuracy and efficiency of data analysis while protecting data privacy. This needs to be studied and improved at technical, legal and institutional levels in order to find a workable solution.
At present, the combination of privacy computing and blockchain technology is developing rapidly. In recent years, with the development of science and technology, the combination of privacy computing and blockchain technology has attracted more attention. Especially with the emergence of cryptocurrencies, it has become more common to combine privacy computing with blockchain technology.
In the future, the development of privacy computing combined with blockchain technology will be faster and faster, and will enter more fields. For example, there will be more applications for privacy protection, and it can be better used in the financial sector to protect the security and convenience of financial transactions. In addition, the combination of privacy computing and blockchain technology can also be applied to the Internet of Things, autonomous driving and other fields to ensure secure communication between devices.
At present, the development trend of privacy computing and blockchain technology is mainly reflected in the following aspects:
**1. Joint application: **Privacy computing technology and blockchain technology are combined to provide better privacy protection and security guarantee in big data analysis.
**2. Smart contract: **Smart contracts supported by blockchain technology can realise the automation and standardisation of privacy computing and improve the efficiency and accuracy of data analysis.
**3. Decentralization: **Through the decentralization characteristics of blockchain technology, the transparency and trust of the privacy computing process can be realised to ensure the fairness and impartiality of data analysis.
**4. High performance computing: **Blockchain technology can support high performance computing, which can effectively improve the speed and efficiency of privacy computing.
**5. Scalability: **Blockchain technology has good scalability, which can support large-scale privacy computing and meet the growing demand for data analysis.
Currently, there are some difficulties in combining privacy computing with blockchain technology.
1. Blockchain technology itself is a distributed ledger technology, which has the advantages of immutable and decentralized, but these advantages also bring some problems, such as the performance, security and fairness of blockchain. In data storage and transaction, it is easy to be hacked and tampered with, which poses a threat to data security.
2. The combination of privacy computing and blockchain technology also has privacy protection problems. Privacy computing involves the processing and analysis of large amounts of data that could expose personal privacy if stored directly on the blockchain, so innovative mechanisms need to be developed to protect privacy.
3. The combination of privacy computing and blockchain technology also needs to address the issues of data legitimacy and accuracy. Due to the distributed nature of blockchain technology, the generation and storage of data are decentralized, so some mechanisms need to be developed to ensure the legitimacy and accuracy of data. The technology of privacy calculation is not mature enough, which will lead to data distortion and precision decline in the process of calculation, and affect the accuracy of data analysis.
4. The combination of privacy computing and blockchain technology requires a large amount of computing resources and storage space, which makes the cost high and is not conducive to widespread application.
5. The compatibility between privacy computing and blockchain technology is also a problem, and unified technical standards are needed to ensure the interoperability and security of data.
Therefore, the development of the combination of privacy computing and blockchain technology still needs to be explored and improved in order to better meet the technical challenges and achieve more efficient and secure data analysis and storage. In general, the combination of privacy computing and blockchain technology still needs to be further studied and explored at the technical level to solve the above technical problems.
In terms of technology, the private computing public chain project has also made great progress. Some projects have adopted advanced encryption technology to achieve multi-party calculation, privacy protection and other functions, providing users with safer and more private data analysis services. According to the data analysis, both the number of users and transaction volume of such projects are on the rise, indicating the growing market demand for private computing public chain technology. In addition, the participants of the private computing public chain project are also increasing, attracting the attention of many well-known enterprises and investment institutions.
In general, the privacy computing public chain project is developing well at present and has broad prospects for development. With the increasing demand for data privacy protection, private computing public chain technology will be applied in more fields to provide better services for users. When we evaluate a private computing public chain, we need to analyse and compare the following aspects:
**1. Availability: **Private computing public blockchain projects need to ensure the availability of data, including preventing network failures and node downtime.
**2. Security: **Private computing public blockchain projects need to ensure the security of data, including preventing third-party attacks and data leaks.
**3. Privacy: **Privacy computing public blockchain projects need to ensure the privacy of data, including symmetric encryption, asymmetric encryption and other technologies to ensure that the data is not leaked.
**4. Openness: **private computing public blockchain projects need open source code to facilitate community participation in development and audit.
**5. Scalability: **Private computing public blockchain projects need to have good scalability and be able to cope with the storage and processing of massive data.
**6. Community activity: **privacy computing public blockchain projects need to have a good degree of community activity, including participation in development and community governance.
**7. Technological innovation: **The private computing public blockchain project needs to have good technological innovation ability and be able to continuously provide new technological solutions.
**8. User friendliness: **including user interface friendliness, operation process rationality, interaction efficiency, etc. In addition, it is also necessary to evaluate the functional completeness of the project, including data storage, access, query, statistical analysis and other aspects.
Summary: Aleo is the first decentralized open source platform to support proprietary and programmable applications. Aleo uses zero-knowledge proof to solve privacy problems. It is a programmable privacy public chain that hides interaction details such as participants, smart contracts and amounts through zero-knowledge proof technology.
Funding: Aleo raised $28 million in Series A funding on April 20, 2021, led by A16z and followed by Coinbase Ventures and Polychain Capital. On February 7, 2022, Aleo announced the completion of a $200 million Series B financing round led by Softbank and Kora Management, followed by A16z and Tiger Fund.
Source: Aleo
Update: Aleo ran Testnet 3 from August to October. The first phase, in August, is for developers, who can start writing, deploying, and executing programs. The second phase (September) is open to proffers who solve Coinbase puzzles (PoSW) for credit. The third phase (October) is for verifiers, who are rewarded by making blocks. Aleo will distribute 25 million Aleo credits (ALEO) to the community of developers, proffers and validators over three phases of Beta 3. Meanwhile, the Aleo team plans to launch Mainnet in the fourth quarter of this year.
Brief introduction: Secret Network is a Layer 1 privacy public chain based on Cosmos SDK and Tendermint core consensus mechanism. It can achieve interoperability with other Cosmos SDK application chains with IBC interfaces through the IBC protocol. The Secret Network mainly realises data privacy through the following main components: Secret Contract and Trusted Execution Environment (TEE).
Source: Secret Network
Funding: $11.5 million led by Arrington Capital and Blocktower Group closed in May 2021. On January 20, Secret Network announced the launch of a $400 million ecosystem fund to strengthen the ecosystem. Investors include Dragonfly Capital, Fenbushi Capital and others.
Summary: Oasis Network is a privacy-enabled, extensible Layer1 blockchain designed to provide support for private, extensible DeFi. The Oasis Network has a tokenized data design through which users can earn rewards by pledging their data in applications supported by the Oasis Network.
Financing: In November 2018, $40 million was raised by well-known venture Capital firms such as A16Z, Polychain Capital, Binance Labs and Pantera Capital. Launched a $160 million eco-development fund in November 2021, funded by FBG, Jump Capital, NGC Ventures, Oasis Foundation, Pantera Capital and other investment institutions. Application scenarios for eco-funds include Defi, NFTs, metacomes, and data tokenization.
Source: Oasis Network
What’s New: Oasis will push the first EVM compliant Privacy ParaTime Sapphire mainnet online, with mainnet upgrades to enhance existing privacy ParaTime ciphers to enable WebAssembly (WASM) -based privacy smart contract functionality.
Introduction: Universal blockchain incubation of privacy and intelligent computing public chain. “Computing interoperability” is its core feature. PlatON builds computing systems assembled from cryptographic algorithms such as verifiable computing, secure multi-party computing, zero-knowledge proof, homomorphic encryption, and blockchain technologies to provide a common infrastructure for artificial intelligence, distributed application developers, data providers, and organizations and individuals with computing needs.
Financing: Since its official launch in San Francisco in July 2018, it has raised more than 50 million dollars in two rounds. The first round of financing was jointly led by Hashkey Capital and Youbi Capital. Hash Global Capital, SNZ Capital, Fundamental Labs and other investors participated in the launch. The second round of financing was initiated by Mr. Liang Xinjun, co-founder of Fosun Group and former President and CEO of Fosun Group. It was led by Hash Global Capital, with participation from Singapore’s OUE Group, Asia’s leading insurance management institutions and other family offices.
Source: PlatON
Update: PlatON 2.0 continues to be updated. PlatON 2.0 will use a three-tier network architecture solution: Layer1 Consensus layer, Layer2 private computing network Metis, and Layer3 AI proxy autonomous network Horae. The three-tier architecture is designed to gather the data, algorithms and computing power needed for private AI computing in a decentralized manner.
In the process of development, the private computing public chain project should focus on technology breakthroughs and the implementation of differentiation strategy to cope with the challenges of market competition. For example, improving the performance and security of privacy computing through continuous technological improvements; Through the development of unique application scenarios and high-quality user experience, to provide users with better services.
A private computing public blockchain project can differentiate itself from the competition by:
In the process of development, advanced privacy protection technologies, such as zero-knowledge proof and multiple signatures, are adopted to ensure the security and privacy of user information. One approach is to use encryption techniques, such as zero-knowledge proof or aggregate signatures, to protect the privacy of transaction data. The technique could prove the legality of a transaction without revealing details.
Another approach is to use blockchain extension technologies, such as sharding, to improve the processing power and security of the network. This technology enables multiple nodes to process transactions simultaneously, improving transaction processing efficiency while reducing the risk of transaction cracking.
Optimize the blockchain architecture and algorithm to make the transaction processing capacity more efficient and improve the scalability and stability of the system. One method is to use efficient privacy computing algorithms, such as zero-knowledge proof and content anonymisation, to protect the privacy of transaction information without affecting the efficiency of transaction processing. Project parties can also adopt faster, high-capacity distributed storage systems to ensure that the blockchain network is capable of large-scale, high-speed transaction processing.
In addition, project parties can improve the scalability and resilience of the network by continuously improving and upgrading the technical architecture of the network, enabling it to cope with the increasing transaction traffic. Finally, the project side can also cooperate with the third party to build a fast and safe payment channel and provide a variety of safe transaction methods to meet the needs of different users and enhance the competitiveness of the project.
Provide users with rich application scenarios, build a sound application ecology, and provide users with convenient and secure blockchain services. Privacy computing public blockchain projects can stand out in the application ecosystem in the following ways:
- Provide users with safe and secure privacy protection services through efficient privacy protection technology, and establish a good reputation.
- Establish partnership, cooperate with other projects to develop applications, expand the application scope and user base.
- Support developers in developing privacy apps by providing quality developer tools and resources to attract more developers.
- Actively participate in industry summits and seminars to showcase project results and attract the attention of investors and partners.
-Strengthen the collection and analysis of users’ needs, and constantly improve project technology and services to meet users’ needs.
The anonymization of DAOs will be an inexorable trend. We will promote a fair, transparent and credible community governance model, and allow users to participate in it to build a good community atmosphere. Privacy protection is a major challenge facing blockchain technology, and many users are reluctant to use blockchain applications for fear of privacy disclosure. Therefore, the public blockchain project of privacy computing can provide more perfect privacy protection mechanism, such as privacy computing technology, anonymous account system, privacy protection protocol, etc., to attract users to join DAO governance.
One way is by emphasising the importance of privacy computing in DAO governance and demonstrating its unique advantages to the community. For example, provide more secure and efficient privacy protection mechanism, and have lower transaction costs compared to other public blockchain projects. At the same time, more technical support and legal compliance can be provided for private computing projects through cooperation with relevant experts and institutions. In addition, more support can be provided for the development of privacy computing projects through communication and interaction with community members, gathering and meeting the needs of the community.
Through continuous marketing and publicity, let more users understand and use the private computing public blockchain project, improve its advantages in the competition. This can attract users’ attention by providing more powerful functions in privacy protection, such as data encryption, identity hiding, etc. This area of anonymous communication and social programs is worth watching.
The development trend of the public blockchain project for privacy computing in 2022 can be summarised in three aspects:
**1. Technological innovation: **Privacy computing technology will be further developed in 2022 to develop more efficient and secure privacy protection technology. For example, mixed encryption technology, private key encryption technology, aggregation signature technology and so on will be used more.
**2. Market expansion: **The private computing public blockchain project will further expand its market share and gradually become the mainstream technology in the blockchain field. For example, Zcash, a private computing project, has become the largest public blockchain project supporting private computing by market capitalization and is expected to remain a leader in 2022.
**3. Application expansion: **The private computing public blockchain project will gradually expand its application field to more industries. For example, privacy computing technology can be applied to the medical industry to protect the security of patients’ personal information; It can also be applied to the DeFi industry to protect user privacy. Privacy computing public blockchain can be used for security audit and protection of smart contracts to ensure privacy security during the execution of smart contracts, and can also be used for DID verification to ensure the security and authenticity of identity information through encrypted storage and security verification of user information. Privacy computing public blockchain can be used for remote access control to ensure the security and controllability of the system through encrypted storage and security verification of access rights, and can also be used for cross-border payments to ensure the security and credibility of transactions through encrypted storage and security verification of transaction information.
In the future, privacy computing technology will be further improved to make it harder for data to be stolen and cracked during transmission. The private computing public blockchain project will be applied more in finance, healthcare, government and other fields to improve data security and audit traceability. At the same time, it will be combined with other blockchain technologies, such as modularity, integration layer, smart contract, etc., to achieve more application scenarios.
It is worth noting that the private computing public blockchain project will face more regulatory challenges and need to communicate and coordinate with relevant departments to ensure the legal compliance of data privacy. There will be more competitors in this field, and it will take constant innovation and upgrading of technology to gain an edge in the market.
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Riding the next waves of blockchain and Web3 innovation
By Kyle, Investment Manager@Bing Ventures
Users enjoy personalised services provided by big data, which brings great convenience to life, but the information collected includes sensitive information such as identity, hobbies, geographical location, and personal income. Once this personal privacy information is leaked, it will bring great security risks. Therefore, the emergence of privacy computing technology provides a solution for user data security.
Privacy Computing refers to a comprehensive technology that realises the separation of data privacy and usability through confidential technologies such as federated learning, secure multi-party, homomorphic encryption, and zero-knowledge proof. In addition to protecting data privacy, it enables data owners to control their own data and obtain valuable algorithmic technology from it.
Under the privacy computing framework, the plaintext data of the participating parties does not leave the local area, so as to protect data security and realise multi-source data cross-domain cooperation, which can effectively solve the problems of data protection and fusion applications. To put it simply, privacy computing realizes “data is available but not visible” and the efficient transformation and circulation of data value on the premise of ensuring that the data itself is not leaked to the outside world.
Privacy computing can help users realise remote computing functions without explicitly disclosing their data. In recent years, the privacy computing industry has grown rapidly. According to the latest data, as of 2020, the global privacy computing market has reached 28.61 billion US dollars, and it is expected that by 2026, the market size will reach 111.89 billion US dollars, with a growth rate of 28.6%.
As the industry has evolved, so have the latest demands in the privacy computing space. Privacy computing also requires powerful computing power to meet users’ requirements for higher efficiency and quality. In addition, trusted computing is also an emerging field in privacy computing. Users can implement trusted computing without exposing data.
Source: SciencDirect
The application fields of privacy computing are also expanding. Traditional application fields are mainly concentrated in the fields of finance, medical care, government, and law. However, with the development of technology, privacy computing has entered new fields.
The development of privacy computing has also promoted the development of related technologies. For example, in privacy computing, cryptography plays an important role. With the development of privacy computing, encryption technology has also made great progress, such as encryption technology based on random numbers and error-correcting codes.
To sum up, the privacy computing industry is developing rapidly, and its core requirements include: (1) users’ needs for privacy protection and security; (2) more powerful computing capabilities to meet users’ requirements; (3) trustworthy computing development. In the future, the privacy computing industry will continue to develop and grow, and continue to meet users’ needs for privacy protection, security, and efficiency.
There are many ways to implement privacy computing technology. The common implementation methods of privacy computing technology include:
1. Encrypted domain computing: By encrypting data and operations, it ensures that the true content of the data will not be revealed during the operation.
2. Anonymous computing: By obfuscating the true source identity of the data, it ensures that the true content of the data will not be revealed during the calculation.
3. Obfuscated computing: By obfuscating the data, it ensures that the true content of the data will not be revealed during the calculation.
Among them, encrypted computing is the most commonly used method. It uses the principle of cryptography to protect the privacy of data by encrypting data. Anonymous computing is another way of privacy protection, which obfuscates user identities during data processing to prevent user privacy from being leaked. Obfuscated computing is a special anonymous computing method, which uses probability theory to add noise in the data processing process, making the result uncertain, thereby protecting user privacy.
From the comparative analysis of advantages and disadvantages, both encrypted domain computing and anonymous computing have certain security. But in the case of complex calculations, the performance of encrypted domain calculations is poor, and the security of anonymous calculations is not particularly high. In contrast, the performance and security of confusing computing are better, but the implementation is more complicated and not suitable for some simple operations.
The realisation of privacy computing technology is inseparable from the following three types of technologies: encryption technology, privacy protection mechanism and enhanced security technology.
Source: Gartner
Encryption is the most commonly used technique in privacy computing. The principle is to use key encryption technology to encrypt the data of each participant, so that the data can only be safely transmitted between participants. The advantage of encryption technology is that it can protect the integrity of data and ensure data security. Its disadvantage is that the confidentiality of the data cannot be guaranteed, since the encrypted data is still visible.
The privacy protection mechanism is also a commonly used privacy computing technology, which uses technologies such as digital signatures and message authentication codes to ensure the confidentiality of data transmission between participants and the integrity of messages. The advantage of the privacy protection mechanism is that it can protect the data security between the participants and ensure the authenticity and integrity of the message. Its disadvantage is that since the data has been encrypted, the computational efficiency between the participating parties will be affected to a certain extent.
Enhanced security technology is a technology for complex privacy computing scenarios. It uses technologies such as identity authentication and expiration date management to protect the data security between participants to ensure that data will not be leaked during transmission. The advantage of hardened security technology is that it can improve the security of data transmission between participating parties. Its disadvantage is that due to the double encryption, the calculation efficiency will be affected to a certain extent.
Through the above analysis, it can be seen that encryption technology and privacy protection mechanism can effectively protect the data security between participants, while enhanced security technology can better protect the data security between participants, but it will affect the computing efficiency. certain influence. Therefore, in different privacy computing scenarios, it is necessary to select the appropriate privacy computing technology according to the actual situation to ensure data security and computing efficiency.
From the perspective of technology classification, privacy computing has the following main directions: first, the privacy computing technology stack with cryptography as the core, including secure multi-party computing, zero-knowledge proof, and homomorphic encryption; Information execution environment and differential privacy and other technologies. We focus on the following mainstream technical solutions, namely: secure multi-party computation (MPC), zero-knowledge proof (ZKP), homomorphic encryption (HE), trusted execution environment (TEE), and federated learning (FL).
In the absence of participants sharing their own data and without a trusted third party, this technology can still perform collaborative computing, ultimately producing valuable analytical content.
Advantage:
Based on the security principle of cryptography, its security is proved by strict cryptography theory, and it is not based on trusting any participant, operator, system, hardware or software. Each participant has absolute control over the data it owns, guarantees that basic data and information will not be leaked, and at the same time has high calculation accuracy and supports programmable general-purpose computing.
Shortcoming:
Multi-party secure computing involves complex cryptographic operations, and computing performance is a major obstacle to its application. With the expansion of the application scale, it is a major challenge for manufacturers to adopt a suitable computing solution to ensure that the computing time and the number of participants show a linear change. From the perspective of security, the goal of multi-party secure computing is to ensure the privacy and security of multi-party data fusion computing. Some traditional security issues, such as access control and transmission security, still need other corresponding technical means to support.
The prover can convince the verifier of the authenticity of the data without revealing the specific data. Zero-knowledge proof can be interactive, that is, the prover has to prove the authenticity of the data once to each verifier; it can also be non-interactive, that is, the prover creates a proof, and anyone who uses this proof can be verified.
Advantage:
The main value of zero-knowledge proof lies in the use of private data sets on transparent public chains such as Ethereum. Blockchain combined with zero-knowledge proof technology allows users and businesses to use private data to execute smart contracts without disclosing specific data content. Zero-knowledge proofs are currently most widely used in blockchains for private transactions. ZCash has already started using zero-knowledge proofs, which hide transaction amounts and sender and receiver addresses.
Shortcoming:
Need to find a trusted third party to create initialisation parameters. If the random numbers used in the creation process are not continuously deleted, then the person who obtained these random numbers can successfully forge the proof. This is the so-called trust setup problem.
Homomorphic encryption is a form of encryption that allows people to perform specific forms of algebraic operations on ciphertext to obtain an encrypted result, and the result obtained by decrypting it is the same as the result of the same operation on plaintext.
Advantage:
This technology allows people to process encrypted data to obtain correct results without decrypting the data during the entire process. Fully homomorphic encryption systems are generally based on basic tools such as lattice theory, which can resist quantum attacks.
Shortcoming:
A core problem that homomorphic encryption needs to solve is that it can support any type of calculation. Arbitrary means addition and multiplication, because all calculation programs abstracted as arithmetic circuit representations can be realised by addition and multiplication.
It has computing and storage functions, and can provide a separate processing environment with security and integrity protection. Programs and data in this environment can be protected at a higher level than the operating system level (OS).
Advantage:
Confidential computing in a trusted execution environment has the advantages of being versatile and efficient, and can seamlessly support general computing frameworks and applications, while computing performance is basically comparable to plaintext computing. It can be used for privacy computing alone or combined with other techniques to preserve privacy.
Shortcoming:
The disadvantage of confidential computing is that the TEE trust chain is bound to CPU manufacturers. At present, hardware technology is in the hands of a few core suppliers such as Intel, Qualcomm, and ARM, which affects the credibility of confidential computing technology. Another disadvantage of confidential computing is that current TEE implementations theoretically have the possibility of side-channel attacks, because TEE shares a large amount of system resources with other untrusted execution environment spaces.
This is a distributed machine learning technique or framework originally proposed by Google. It realizes multi-party joint learning and training through the circulation and processing of intermediate encrypted data without the local original data leaving the database. At present, federated learning technology is usually combined with secure multi-party computing technology and blockchain technology.
Advantage:
It can solve the problem of single data features in the training phase, so as to obtain a model with better performance than that trained by using its own data set.
Shortcoming:
It has security problems and communication efficiency problems. Because building a basic neural network model from the underlying code is usually time-consuming and labor-intensive, most companies currently obtain basic models from open source platforms or purchase basic models from third-party platforms. Such basic models themselves may be implanted with viruses. At the same time, the academic community has not yet strictly defined the security effect of federated learning.
It is still possible to deduce the data information of each participant by using the gradient and weight information collected by the central server. In addition, the federated learning mechanism assumes that all participants are trusted parties, and there is no way to prevent a participant from maliciously providing false data or even diseased data, which will cause irreversible damage to the final training model.
In today’s society, data security and privacy protection have become indispensable demands, and privacy computing has become one of the best solutions for data security and privacy protection. The industry value of privacy computing technology is mainly reflected in the following aspects:
**1. Improve data protection and privacy protection capabilities. **Privacy computing technology can protect data privacy and security, avoid data leakage and abuse.
**2. Improve the accuracy and efficiency of data analysis and machine learning. **Privacy computing technology can improve the accuracy and efficiency of data analysis and machine learning, make models more accurate and improve the accuracy of decisions.
**3. Promote the development of big data analysis and machine learning. **Privacy computing technology can promote the development of big data analysis and machine learning, support more data analysis and machine learning applications, and expand application scenarios.
4. Meet the needs of the government and society for data security and privacy protection.
Specifically, firstly, private computing technology can help enterprises improve the efficiency of data analysis. Traditional data analysis methods need to store data together, which increases the risk of data security. Privacy computing technology can carry out data analysis without moving the original data, avoiding data loss and leakage. In this way, enterprises can conduct data analysis more quickly, and save a lot of time and cost.
Secondly, private computing technology can help enterprises improve the accuracy of data analysis. Centralized data storage will lead to data confusion, resulting in inaccurate analysis results. The privacy computing technology can analyze the data without damaging the privacy of the original data, making the analysis results more accurate. In this way, enterprises can rely on accurate data analysis results to make more effective business decisions.
Source: Nature
Privacy computing may be used in the following application scenarios:
**1. Medical data analysis: **Improve the quality of medical services through data analysis, while ensuring the privacy of patients. Privacy computing technology can be used for data analysis under the premise of ensuring patient privacy.
**2. Social media analysis: **Learn users’ preferences through data analysis, and make recommendations and advertising. Privacy computing technology can be used to analyze data under the premise of ensuring user privacy.
**3. Financial data analysis: **improve the risk management ability of financial institutions through data analysis, while protecting customer privacy. Privacy computing technology can be adopted to ensure the privacy of customers under the premise of data analysis.
**4. Consumer behavior analysis: **understand consumers’ needs and preferences through data analysis, and conduct product marketing and pricing strategies. Privacy computing technology can be used to analyze data under the premise of ensuring consumer privacy.
Privacy computing has a wide range of applications in the blockchain industry.
**1. Financial services: **Privacy computing can be used for user authentication, transaction records, etc.
**2. Smart contracts: **Privacy computing can be used to develop smart contracts.
**3. Social media: **Privacy computing can be used for user authentication, data analysis and storage in social media.
**4. Supply chain: **Privacy computing can be used for data storage, trajectory tracking and warehousing in the supply chain.
**5. Data analysis: **Privacy computing can be used for data analysis to ensure data security and privacy protection.
**6. Healthcare: **Privacy computing can be used for data storage, trajectory tracking, and warehousing in healthcare.
**7. Digital identity: **Privacy computing can be used for the development of digital identities.
These are the detailed application scenarios of privacy computing in the blockchain industry. In short, privacy computing is an important technology. It can provide better data analysis capabilities for a wide range of industries while protecting data privacy.
Privacy computing can not only ensure the security and privacy of data interaction between various organizations in the industry, but also eliminate the concerns of individuals or organisations about data security, so as to obtain more data support.
There are three main bottlenecks in the development of privacy computing:
**1. Technical difficulties: **Privacy computing requires the combination of data analysis and privacy protection, which requires a series of new algorithms and technologies to achieve. At present, this aspect of technology is not fully mature, and still needs to continue to be explored and improved.
**2. Lack of standards: **As an emerging field, privacy computing lacks unified standards and specifications, which makes it difficult for different technical solutions to be compatible and interoperable. This hinders the popularity and application of private computing.
**3. Trust problem: **Privacy computing involves sensitive personal privacy. If there is no sufficient trust mechanism, it is difficult to gain the trust and acceptance of users. At present, there is no perfect trust system in the field of privacy computing, which requires in-depth research in the aspects of technology and management.
Privacy computing technology currently faces several challenges:
**1. Balance between data protection and privacy protection. **With the development of big data analysis technology, data analysis often involves a large amount of sensitive personal information. Hence, how to protect the privacy of users while ensuring the accuracy of analysis has become an important issue.
**2. Security of privacy computing. **Privacy computing requires the use of distributed computing technology. Thus, ensuring that data is not tampered or leaked in the process of distributed computing becomes a technical problem.
**3. Scalability of privacy computing. **With the increase of data volume, it is a challenge for privacy computing systems to ensure the scalability of analysis while maintaining privacy.
**4. The realization of privacy computing. **Privacy computing systems need to be deeply studied in algorithms, architecture and implementation, and how to achieve efficient and accurate privacy computing has become a technical problem.
At present, there are several missing problems in the field of privacy computing, such as:
**1. Reliability problems. **Privacy computing should ensure the accuracy and reliability of data as well as the privacy of data. But at present, many privacy computing technologies have reliability problems, such as the attack and cracking of encryption algorithms, the robustness of privacy obfuscation algorithms and so on.
**2. Scalability problem. **Privacy computing technology needs to support large-scale data processing, but many privacy computing methods have scalability problems, such as high computational complexity and performance bottlenecks.
**3. Verifiability issues. **Privacy computation needs to verify the data privacy protection mechanism, but many privacy computation methods currently have the problem of verifiability, such as the failure to verify whether the data is tampered with, whether the privacy protection mechanism is feasible, etc.
**4. Storage problems. **On the one hand, with the continuous development and application of data analysis technology, the way of data storage, analysis and utilisation is also constantly changing. In this context, mechanisms to protect the privacy of personal data need to evolve to adapt to different ways of storing, analysing and exploiting data. However, at present, due to the lack of coordination between different departments of the data privacy protection mechanism, the progress of the update and improvement of the privacy protection mechanism is slow, which cannot timely adapt to the development of data analysis technology.
On the other hand, with the development of data analysis technology, the storage, analysis and utilisation of data are becoming increasingly diversified. In this context, mechanisms to protect the privacy of personal data need to cover many different ways of storing, analysing and exploiting data. However, at present, due to the lack of coordination between different departments of the data privacy protection mechanism, there are overlapping and contradictory privacy protection mechanisms in various departments, which makes the implementation and execution of the privacy protection mechanism chaotic.
At present, there is a problem of trust in privacy computing, because in the process of data analysis, the data needs to be comprehensively analyzed, and often the data of multiple parties needs to be jointly analysed, which requires data privacy issues. For example, in the banking industry, banks usually need to cooperate with multiple parties to analyse customer credit information. However, if the sensitive data of customers is directly exposed to external partners, the risk of privacy disclosure will be brought.
In order to solve this trust problem, some solutions have been proposed, such as blockchain technology, encryption technology, anonymous technology and so on. These technologies can effectively protect data privacy, but they also have some limitations. For example, the performance of blockchain technology is limited and cannot cope with the demands of large-scale data analysis; Encryption technology may affect data analysis and affect the accuracy of data analysis.
Therefore, to solve the trust problem of privacy computing, it is necessary to improve the accuracy and efficiency of data analysis while protecting data privacy. This needs to be studied and improved at technical, legal and institutional levels in order to find a workable solution.
At present, the combination of privacy computing and blockchain technology is developing rapidly. In recent years, with the development of science and technology, the combination of privacy computing and blockchain technology has attracted more attention. Especially with the emergence of cryptocurrencies, it has become more common to combine privacy computing with blockchain technology.
In the future, the development of privacy computing combined with blockchain technology will be faster and faster, and will enter more fields. For example, there will be more applications for privacy protection, and it can be better used in the financial sector to protect the security and convenience of financial transactions. In addition, the combination of privacy computing and blockchain technology can also be applied to the Internet of Things, autonomous driving and other fields to ensure secure communication between devices.
At present, the development trend of privacy computing and blockchain technology is mainly reflected in the following aspects:
**1. Joint application: **Privacy computing technology and blockchain technology are combined to provide better privacy protection and security guarantee in big data analysis.
**2. Smart contract: **Smart contracts supported by blockchain technology can realise the automation and standardisation of privacy computing and improve the efficiency and accuracy of data analysis.
**3. Decentralization: **Through the decentralization characteristics of blockchain technology, the transparency and trust of the privacy computing process can be realised to ensure the fairness and impartiality of data analysis.
**4. High performance computing: **Blockchain technology can support high performance computing, which can effectively improve the speed and efficiency of privacy computing.
**5. Scalability: **Blockchain technology has good scalability, which can support large-scale privacy computing and meet the growing demand for data analysis.
Currently, there are some difficulties in combining privacy computing with blockchain technology.
1. Blockchain technology itself is a distributed ledger technology, which has the advantages of immutable and decentralized, but these advantages also bring some problems, such as the performance, security and fairness of blockchain. In data storage and transaction, it is easy to be hacked and tampered with, which poses a threat to data security.
2. The combination of privacy computing and blockchain technology also has privacy protection problems. Privacy computing involves the processing and analysis of large amounts of data that could expose personal privacy if stored directly on the blockchain, so innovative mechanisms need to be developed to protect privacy.
3. The combination of privacy computing and blockchain technology also needs to address the issues of data legitimacy and accuracy. Due to the distributed nature of blockchain technology, the generation and storage of data are decentralized, so some mechanisms need to be developed to ensure the legitimacy and accuracy of data. The technology of privacy calculation is not mature enough, which will lead to data distortion and precision decline in the process of calculation, and affect the accuracy of data analysis.
4. The combination of privacy computing and blockchain technology requires a large amount of computing resources and storage space, which makes the cost high and is not conducive to widespread application.
5. The compatibility between privacy computing and blockchain technology is also a problem, and unified technical standards are needed to ensure the interoperability and security of data.
Therefore, the development of the combination of privacy computing and blockchain technology still needs to be explored and improved in order to better meet the technical challenges and achieve more efficient and secure data analysis and storage. In general, the combination of privacy computing and blockchain technology still needs to be further studied and explored at the technical level to solve the above technical problems.
In terms of technology, the private computing public chain project has also made great progress. Some projects have adopted advanced encryption technology to achieve multi-party calculation, privacy protection and other functions, providing users with safer and more private data analysis services. According to the data analysis, both the number of users and transaction volume of such projects are on the rise, indicating the growing market demand for private computing public chain technology. In addition, the participants of the private computing public chain project are also increasing, attracting the attention of many well-known enterprises and investment institutions.
In general, the privacy computing public chain project is developing well at present and has broad prospects for development. With the increasing demand for data privacy protection, private computing public chain technology will be applied in more fields to provide better services for users. When we evaluate a private computing public chain, we need to analyse and compare the following aspects:
**1. Availability: **Private computing public blockchain projects need to ensure the availability of data, including preventing network failures and node downtime.
**2. Security: **Private computing public blockchain projects need to ensure the security of data, including preventing third-party attacks and data leaks.
**3. Privacy: **Privacy computing public blockchain projects need to ensure the privacy of data, including symmetric encryption, asymmetric encryption and other technologies to ensure that the data is not leaked.
**4. Openness: **private computing public blockchain projects need open source code to facilitate community participation in development and audit.
**5. Scalability: **Private computing public blockchain projects need to have good scalability and be able to cope with the storage and processing of massive data.
**6. Community activity: **privacy computing public blockchain projects need to have a good degree of community activity, including participation in development and community governance.
**7. Technological innovation: **The private computing public blockchain project needs to have good technological innovation ability and be able to continuously provide new technological solutions.
**8. User friendliness: **including user interface friendliness, operation process rationality, interaction efficiency, etc. In addition, it is also necessary to evaluate the functional completeness of the project, including data storage, access, query, statistical analysis and other aspects.
Summary: Aleo is the first decentralized open source platform to support proprietary and programmable applications. Aleo uses zero-knowledge proof to solve privacy problems. It is a programmable privacy public chain that hides interaction details such as participants, smart contracts and amounts through zero-knowledge proof technology.
Funding: Aleo raised $28 million in Series A funding on April 20, 2021, led by A16z and followed by Coinbase Ventures and Polychain Capital. On February 7, 2022, Aleo announced the completion of a $200 million Series B financing round led by Softbank and Kora Management, followed by A16z and Tiger Fund.
Source: Aleo
Update: Aleo ran Testnet 3 from August to October. The first phase, in August, is for developers, who can start writing, deploying, and executing programs. The second phase (September) is open to proffers who solve Coinbase puzzles (PoSW) for credit. The third phase (October) is for verifiers, who are rewarded by making blocks. Aleo will distribute 25 million Aleo credits (ALEO) to the community of developers, proffers and validators over three phases of Beta 3. Meanwhile, the Aleo team plans to launch Mainnet in the fourth quarter of this year.
Brief introduction: Secret Network is a Layer 1 privacy public chain based on Cosmos SDK and Tendermint core consensus mechanism. It can achieve interoperability with other Cosmos SDK application chains with IBC interfaces through the IBC protocol. The Secret Network mainly realises data privacy through the following main components: Secret Contract and Trusted Execution Environment (TEE).
Source: Secret Network
Funding: $11.5 million led by Arrington Capital and Blocktower Group closed in May 2021. On January 20, Secret Network announced the launch of a $400 million ecosystem fund to strengthen the ecosystem. Investors include Dragonfly Capital, Fenbushi Capital and others.
Summary: Oasis Network is a privacy-enabled, extensible Layer1 blockchain designed to provide support for private, extensible DeFi. The Oasis Network has a tokenized data design through which users can earn rewards by pledging their data in applications supported by the Oasis Network.
Financing: In November 2018, $40 million was raised by well-known venture Capital firms such as A16Z, Polychain Capital, Binance Labs and Pantera Capital. Launched a $160 million eco-development fund in November 2021, funded by FBG, Jump Capital, NGC Ventures, Oasis Foundation, Pantera Capital and other investment institutions. Application scenarios for eco-funds include Defi, NFTs, metacomes, and data tokenization.
Source: Oasis Network
What’s New: Oasis will push the first EVM compliant Privacy ParaTime Sapphire mainnet online, with mainnet upgrades to enhance existing privacy ParaTime ciphers to enable WebAssembly (WASM) -based privacy smart contract functionality.
Introduction: Universal blockchain incubation of privacy and intelligent computing public chain. “Computing interoperability” is its core feature. PlatON builds computing systems assembled from cryptographic algorithms such as verifiable computing, secure multi-party computing, zero-knowledge proof, homomorphic encryption, and blockchain technologies to provide a common infrastructure for artificial intelligence, distributed application developers, data providers, and organizations and individuals with computing needs.
Financing: Since its official launch in San Francisco in July 2018, it has raised more than 50 million dollars in two rounds. The first round of financing was jointly led by Hashkey Capital and Youbi Capital. Hash Global Capital, SNZ Capital, Fundamental Labs and other investors participated in the launch. The second round of financing was initiated by Mr. Liang Xinjun, co-founder of Fosun Group and former President and CEO of Fosun Group. It was led by Hash Global Capital, with participation from Singapore’s OUE Group, Asia’s leading insurance management institutions and other family offices.
Source: PlatON
Update: PlatON 2.0 continues to be updated. PlatON 2.0 will use a three-tier network architecture solution: Layer1 Consensus layer, Layer2 private computing network Metis, and Layer3 AI proxy autonomous network Horae. The three-tier architecture is designed to gather the data, algorithms and computing power needed for private AI computing in a decentralized manner.
In the process of development, the private computing public chain project should focus on technology breakthroughs and the implementation of differentiation strategy to cope with the challenges of market competition. For example, improving the performance and security of privacy computing through continuous technological improvements; Through the development of unique application scenarios and high-quality user experience, to provide users with better services.
A private computing public blockchain project can differentiate itself from the competition by:
In the process of development, advanced privacy protection technologies, such as zero-knowledge proof and multiple signatures, are adopted to ensure the security and privacy of user information. One approach is to use encryption techniques, such as zero-knowledge proof or aggregate signatures, to protect the privacy of transaction data. The technique could prove the legality of a transaction without revealing details.
Another approach is to use blockchain extension technologies, such as sharding, to improve the processing power and security of the network. This technology enables multiple nodes to process transactions simultaneously, improving transaction processing efficiency while reducing the risk of transaction cracking.
Optimize the blockchain architecture and algorithm to make the transaction processing capacity more efficient and improve the scalability and stability of the system. One method is to use efficient privacy computing algorithms, such as zero-knowledge proof and content anonymisation, to protect the privacy of transaction information without affecting the efficiency of transaction processing. Project parties can also adopt faster, high-capacity distributed storage systems to ensure that the blockchain network is capable of large-scale, high-speed transaction processing.
In addition, project parties can improve the scalability and resilience of the network by continuously improving and upgrading the technical architecture of the network, enabling it to cope with the increasing transaction traffic. Finally, the project side can also cooperate with the third party to build a fast and safe payment channel and provide a variety of safe transaction methods to meet the needs of different users and enhance the competitiveness of the project.
Provide users with rich application scenarios, build a sound application ecology, and provide users with convenient and secure blockchain services. Privacy computing public blockchain projects can stand out in the application ecosystem in the following ways:
- Provide users with safe and secure privacy protection services through efficient privacy protection technology, and establish a good reputation.
- Establish partnership, cooperate with other projects to develop applications, expand the application scope and user base.
- Support developers in developing privacy apps by providing quality developer tools and resources to attract more developers.
- Actively participate in industry summits and seminars to showcase project results and attract the attention of investors and partners.
-Strengthen the collection and analysis of users’ needs, and constantly improve project technology and services to meet users’ needs.
The anonymization of DAOs will be an inexorable trend. We will promote a fair, transparent and credible community governance model, and allow users to participate in it to build a good community atmosphere. Privacy protection is a major challenge facing blockchain technology, and many users are reluctant to use blockchain applications for fear of privacy disclosure. Therefore, the public blockchain project of privacy computing can provide more perfect privacy protection mechanism, such as privacy computing technology, anonymous account system, privacy protection protocol, etc., to attract users to join DAO governance.
One way is by emphasising the importance of privacy computing in DAO governance and demonstrating its unique advantages to the community. For example, provide more secure and efficient privacy protection mechanism, and have lower transaction costs compared to other public blockchain projects. At the same time, more technical support and legal compliance can be provided for private computing projects through cooperation with relevant experts and institutions. In addition, more support can be provided for the development of privacy computing projects through communication and interaction with community members, gathering and meeting the needs of the community.
Through continuous marketing and publicity, let more users understand and use the private computing public blockchain project, improve its advantages in the competition. This can attract users’ attention by providing more powerful functions in privacy protection, such as data encryption, identity hiding, etc. This area of anonymous communication and social programs is worth watching.
The development trend of the public blockchain project for privacy computing in 2022 can be summarised in three aspects:
**1. Technological innovation: **Privacy computing technology will be further developed in 2022 to develop more efficient and secure privacy protection technology. For example, mixed encryption technology, private key encryption technology, aggregation signature technology and so on will be used more.
**2. Market expansion: **The private computing public blockchain project will further expand its market share and gradually become the mainstream technology in the blockchain field. For example, Zcash, a private computing project, has become the largest public blockchain project supporting private computing by market capitalization and is expected to remain a leader in 2022.
**3. Application expansion: **The private computing public blockchain project will gradually expand its application field to more industries. For example, privacy computing technology can be applied to the medical industry to protect the security of patients’ personal information; It can also be applied to the DeFi industry to protect user privacy. Privacy computing public blockchain can be used for security audit and protection of smart contracts to ensure privacy security during the execution of smart contracts, and can also be used for DID verification to ensure the security and authenticity of identity information through encrypted storage and security verification of user information. Privacy computing public blockchain can be used for remote access control to ensure the security and controllability of the system through encrypted storage and security verification of access rights, and can also be used for cross-border payments to ensure the security and credibility of transactions through encrypted storage and security verification of transaction information.
In the future, privacy computing technology will be further improved to make it harder for data to be stolen and cracked during transmission. The private computing public blockchain project will be applied more in finance, healthcare, government and other fields to improve data security and audit traceability. At the same time, it will be combined with other blockchain technologies, such as modularity, integration layer, smart contract, etc., to achieve more application scenarios.
It is worth noting that the private computing public blockchain project will face more regulatory challenges and need to communicate and coordinate with relevant departments to ensure the legal compliance of data privacy. There will be more competitors in this field, and it will take constant innovation and upgrading of technology to gain an edge in the market.
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By Kyle, Investment Manager@Bing Ventures Like Stablecoin, on-chain asset management has been one of the first blockchain scenarios to land. The DAO’s organizational structure naturally removes organizational barriers to decentralized asset management. Enzyme, a pioneer of multi-chain asset management protocols, was created in 2016. More importantly, it can use tokens to incentivise asset issuers and liquidity participants to tap into more decentralized financial scenarios. From flatter inves...
Riding the next waves of blockchain and Web3 innovation
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