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In the digital age, protecting data privacy has become more crucial than ever before. With the rapid advancement of big data, cloud computing, and Internet of Things technologies, the collection, storage, and analysis of personal information have become increasingly prevalent. However, this has also brought about the risks of data breaches and misuse.
FHE, or Fully Homomorphic Encryption, allows direct computation on encrypted data, and the computed results are consistent with the results of performing the same computation on the original data. This means that we can process and analyze data without exposing the original data, providing a new solution for protecting data privacy and integrity.
The theoretical foundation of FHE can be traced back to 1978 when Rivest et al. proposed the first homomorphic encryption problem. It wasn't until 2009 that Gentry, in his doctoral thesis, constructed the first realizable fully homomorphic encryption scheme, marking the beginning of the flourishing development of FHE research. Early FHE schemes had extremely low computational efficiency, unable to meet practical application requirements. With researchers continuously proposing optimization schemes, the performance of FHE has been significantly improved, propelling FHE research towards enhancing efficiency, reducing computational complexity, and expanding application scenarios.
Ideal Lattice-based FHE Schemes
This is currently recognized as the most practical and efficient construction method for FHE. The main idea is to leverage the algebraic structure of ring operations, completing encryption and decryption through modular arithmetic and factorization of ideal factors in the ring. Representative schemes include BGV, BFV, and CKKS, among others. The advantage of these schemes lies in their high computational efficiency, while the drawback is the need for larger key and ciphertext spaces.
Matrix-based FHE Schemes
These schemes encode plaintext information into matrices and achieve homomorphic properties through matrix operations. Representative schemes include GSW and HiNC, characterized by high security but relatively low efficiency.
NTRU-based FHE Schemes
NTRU (Number Theory Research Unit) has a good algebraic structure and cyclic symmetry, enabling the construction of efficient FHE schemes such as YASHE and NTRU-FHE. The advantage of these schemes is their relatively small key and ciphertext sizes, making them suitable for resource-constrained environments.
FHE Schemes based on Learning With Errors (LWE/LWR)
These schemes are constructed based on the LWE/LWR hardness problems, such as FHEW and TFHE. These schemes prioritize theoretical innovation and can achieve extremely strong security, but their practicality is limited.
FHE and ZKP are both cryptographic techniques, but they are more or less complementary.
ZKP allows a prover to demonstrate to a verifier that a statement is correct without revealing any details about the statement. The verifier can verify the correctness and completeness of the computation without needing to re-execute it. Although ZKP can prove correctness without revealing information, its inputs are typically in plaintext form, which may lead to privacy leaks.
The introduction of FHE solves this problem. FHE enables arbitrary computation on encrypted data without decryption, thereby protecting data privacy. However, FHE cannot independently ensure the correctness and reliability of the computation, which is precisely the problem that ZKP solves.
By combining FHE and ZKP, the advantages of both techniques can be leveraged. FHE protects the privacy of input data and the computation process, while ZKP provides an cryptographic proof of correctness, legality, and auditability for FHE computations. Ultimately, this combination achieves truly secure and trustworthy private computation, which is highly valuable for privacy-preserving applications such as sensitive data processing and multi-party computation.
Zama is an open-source cryptography company building cutting-edge FHE solutions for blockchain and AI. They offer four major open-source solutions:
TFHE-rs is a Rust implementation of fully homomorphic encryption over the Torus, used for performing Boolean and integer operations on encrypted data. The TFHE-rs library implements Zama's variant of TFHE, which enables all necessary homomorphic operations, such as addition and function evaluation through programmable bootstrapping.
Concrete is an open-source FHE framework that includes a TFHE compiler as part of the framework. This compiler translates regular programming code into executable instructions that a computer can use with FHE, allowing developers to easily write FHE programs. Computations on FHE-encrypted data may introduce substantial noise, leading to errors. Concrete has a very low default error probability, and developers can flexibly modify the parameters of this error probability.
Concrete ML is an open-source Privacy-Preserving Machine Learning (PPML) tool built on Concrete, enabling developers to integrate FHE into machine learning models without requiring cryptographic knowledge.
fhEVM introduces FHE into the EVM ecosystem, allowing developers to execute encrypted smart contracts on-chain while still preserving the composability of smart contracts under the premise of protecting on-chain data privacy. fhEVM integrates TFHE-rs and introduces a new TFHE Solidity library, enabling developers to perform computations on encrypted data using Solidity.
Fhenix is the first Layer 2 Rollup based on FHE, built upon Zama's TFHE-rs. It has developed its own cryptographic computation library, fheOS, which includes precompiled opcodes for common encryption operations, enabling smart contracts to use FHE primitives on-chain. fheOS is also responsible for communication and authentication between the rollup and the Threshold Service Network (TSN) for decryption and re-encryption requests, while proving the legitimacy proof of the decryption requests. The fheOS library aims to be integrated as an extension into any existing EVM network, while being fully compatible with the EVM.
Fhenix's consensus mechanism uses Arbitrum's Nitro prover. The reason for choosing a fraud proof approach is that the underlying structures of FHE and zkSNARK are different, and using ZKP to verify FHE computations would be extremely computationally intensive, making it almost impossible to achieve at the current technological stage.
Fhenix has recently collaborated with EigenLayer to develop FHE coprocessors, introducing FHE computations into other public chains, L2s, and L3s. Since Fhenix employs fraud proofs with a 7-day challenge period, EigenLayer's services can help coprocessors achieve fast transaction confirmations, significantly improving performance.
Inco is a modular confidential computing Layer 1 that can serve as a universal confidentiality layer for Web3. It supports fhEVM, enabling developers to rapidly build confidential DApps using the Solidity language and development tools within the Ethereum ecosystem. Additionally, Inco provides Confidentiality as a Service (CaaS) to EVM and Cosmos chains lacking native encryption through bridging and the IBC protocol. The CaaS service primarily offers three functionalities:
On-chain encrypted state: Directly store encrypted data on-chain without off-chain storage.
Composable encrypted state: Perform state transitions on encrypted data entirely on-chain without decryption.
On-chain randomness: Generate randomness for applications on-chain without the need for external randomness services, allowing for direct on-chain application development.
Currently, Inco has several use cases, including gaming, NFTs, RWA, voting governance, and DID.
Mind Network is the first FHE Restaking Layer tailored specifically for AI and PoS networks. As a Restaking Layer, it accepts restaking tokens from ETH, BTC, and AI bluechips for staking. Simultaneously, as an FHE validation network, it utilizes FHE technology to validate node data and reach consensus, ensuring data integrity and security. Mind Network provides economic security guarantees for decentralized AI, DePIN, EigenLayer AVS, Babylon AVS, and critical PoS networks, maintaining the overall system's consensus and trustworthiness.
Restaking Layer: Collaborating with EigenLayer, StakeStone, Renzo, Babylon, Ankr, and others, Mind Network derives security from the Ethereum and Bitcoin networks. It also collaborates with Chainlink CCIP, Connext, and others to enable cross-chain remote restaking.
Security Layer: Mind Network introduces FHE-enhanced validators to ensure end-to-end encryption during the validation and consensus computation processes, and further enhance security by integrating the modular fhEVM of Fhenix and Inco.
Consensus Layer: Mind Network introduces a Proof of Intelligence (PoI) consensus mechanism designed specifically for AI tasks, ensuring fair and secure reward distribution among FHE validators. Additionally, Mind Network is collaborating with AltLayer, EigenDA, and Arbitrum Orbit to launch a Rollup chain, enhancing consensus computation with lower costs and faster performance.
Privasea is a DePIN+AI network for FHE machine learning, whose technical architecture includes the following core components:
HESea Library: An advanced FHE library that provides secure computation capabilities on encrypted data. This library supports various FHE schemes such as TFHE, CKKS, BGV/BFV, and others.
Privasea API: The application programming interface for the Privasea AI network, offering a suite of functionalities and endpoints to simplify data submission, model training, and prediction operations, while ensuring data encryption during transmission and processing.
Privanetix: A decentralized computation network composed of multiple high-performance computing nodes capable of efficiently processing encrypted data. Each node integrates the HESea library to ensure data privacy and computational performance.
Privasea Smart Contract Suite: A blockchain-based incentive mechanism that tracks the registration and contributions of Privanetix nodes through smart contracts, validates computations, and distributes rewards, ensuring participant engagement and fairness.
Privasea has launched the ImHuman application, which develops a Proof of Human based on FHE, aiming to prove that users are human beings to protect their digital identities from impersonation by bots and artificial intelligence. Users can verify their human identity through facial biometric recognition technology, generating a unique NFT as proof of their individual human identity. With the ImHuman application, users can securely attest their personal identities on Web3 and Web2 platforms without revealing personal details.
In the DeFi domain, FHE enables transactions and fund flows to take place without disclosing sensitive financial information, thereby protecting user privacy and reducing market risks. It may also serve as one of the effective solutions to the MEV problem. For fully on-chain games, FHE ensures that player scores, game progress, and other data are protected in an encrypted state, while simultaneously allowing game logic to be executed on-chain without exposing the data, enhancing fairness and security of the games. In the AI field, FHE allows for the analysis and model training on encrypted data, not only protecting data privacy but also promoting cross-institutional data sharing and collaboration, driving the development of more secure and compliant AI applications.
While FHE still faces numerous challenges in practicality and efficiency, its unique theoretical foundation brings hope for overcoming these bottlenecks. In the future, FHE is expected to significantly enhance performance and expand application use cases through algorithm optimization, hardware acceleration, and other approaches, providing a more robust foundation for data privacy protection and secure computation.
https://github.com/zama-ai/tfhe-rs
https://github.com/zama-ai/concrete
https://github.com/zama-ai/concrete-ml
https://github.com/zama-ai/fhevm
https://docs.mindnetwork.xyz/minddocs
https://drive.google.com/file/d/1jbxWMgEziupt119gvM1n0Mu8sDdM7VWF/view
In the digital age, protecting data privacy has become more crucial than ever before. With the rapid advancement of big data, cloud computing, and Internet of Things technologies, the collection, storage, and analysis of personal information have become increasingly prevalent. However, this has also brought about the risks of data breaches and misuse.
FHE, or Fully Homomorphic Encryption, allows direct computation on encrypted data, and the computed results are consistent with the results of performing the same computation on the original data. This means that we can process and analyze data without exposing the original data, providing a new solution for protecting data privacy and integrity.
The theoretical foundation of FHE can be traced back to 1978 when Rivest et al. proposed the first homomorphic encryption problem. It wasn't until 2009 that Gentry, in his doctoral thesis, constructed the first realizable fully homomorphic encryption scheme, marking the beginning of the flourishing development of FHE research. Early FHE schemes had extremely low computational efficiency, unable to meet practical application requirements. With researchers continuously proposing optimization schemes, the performance of FHE has been significantly improved, propelling FHE research towards enhancing efficiency, reducing computational complexity, and expanding application scenarios.
Ideal Lattice-based FHE Schemes
This is currently recognized as the most practical and efficient construction method for FHE. The main idea is to leverage the algebraic structure of ring operations, completing encryption and decryption through modular arithmetic and factorization of ideal factors in the ring. Representative schemes include BGV, BFV, and CKKS, among others. The advantage of these schemes lies in their high computational efficiency, while the drawback is the need for larger key and ciphertext spaces.
Matrix-based FHE Schemes
These schemes encode plaintext information into matrices and achieve homomorphic properties through matrix operations. Representative schemes include GSW and HiNC, characterized by high security but relatively low efficiency.
NTRU-based FHE Schemes
NTRU (Number Theory Research Unit) has a good algebraic structure and cyclic symmetry, enabling the construction of efficient FHE schemes such as YASHE and NTRU-FHE. The advantage of these schemes is their relatively small key and ciphertext sizes, making them suitable for resource-constrained environments.
FHE Schemes based on Learning With Errors (LWE/LWR)
These schemes are constructed based on the LWE/LWR hardness problems, such as FHEW and TFHE. These schemes prioritize theoretical innovation and can achieve extremely strong security, but their practicality is limited.
FHE and ZKP are both cryptographic techniques, but they are more or less complementary.
ZKP allows a prover to demonstrate to a verifier that a statement is correct without revealing any details about the statement. The verifier can verify the correctness and completeness of the computation without needing to re-execute it. Although ZKP can prove correctness without revealing information, its inputs are typically in plaintext form, which may lead to privacy leaks.
The introduction of FHE solves this problem. FHE enables arbitrary computation on encrypted data without decryption, thereby protecting data privacy. However, FHE cannot independently ensure the correctness and reliability of the computation, which is precisely the problem that ZKP solves.
By combining FHE and ZKP, the advantages of both techniques can be leveraged. FHE protects the privacy of input data and the computation process, while ZKP provides an cryptographic proof of correctness, legality, and auditability for FHE computations. Ultimately, this combination achieves truly secure and trustworthy private computation, which is highly valuable for privacy-preserving applications such as sensitive data processing and multi-party computation.
Zama is an open-source cryptography company building cutting-edge FHE solutions for blockchain and AI. They offer four major open-source solutions:
TFHE-rs is a Rust implementation of fully homomorphic encryption over the Torus, used for performing Boolean and integer operations on encrypted data. The TFHE-rs library implements Zama's variant of TFHE, which enables all necessary homomorphic operations, such as addition and function evaluation through programmable bootstrapping.
Concrete is an open-source FHE framework that includes a TFHE compiler as part of the framework. This compiler translates regular programming code into executable instructions that a computer can use with FHE, allowing developers to easily write FHE programs. Computations on FHE-encrypted data may introduce substantial noise, leading to errors. Concrete has a very low default error probability, and developers can flexibly modify the parameters of this error probability.
Concrete ML is an open-source Privacy-Preserving Machine Learning (PPML) tool built on Concrete, enabling developers to integrate FHE into machine learning models without requiring cryptographic knowledge.
fhEVM introduces FHE into the EVM ecosystem, allowing developers to execute encrypted smart contracts on-chain while still preserving the composability of smart contracts under the premise of protecting on-chain data privacy. fhEVM integrates TFHE-rs and introduces a new TFHE Solidity library, enabling developers to perform computations on encrypted data using Solidity.
Fhenix is the first Layer 2 Rollup based on FHE, built upon Zama's TFHE-rs. It has developed its own cryptographic computation library, fheOS, which includes precompiled opcodes for common encryption operations, enabling smart contracts to use FHE primitives on-chain. fheOS is also responsible for communication and authentication between the rollup and the Threshold Service Network (TSN) for decryption and re-encryption requests, while proving the legitimacy proof of the decryption requests. The fheOS library aims to be integrated as an extension into any existing EVM network, while being fully compatible with the EVM.
Fhenix's consensus mechanism uses Arbitrum's Nitro prover. The reason for choosing a fraud proof approach is that the underlying structures of FHE and zkSNARK are different, and using ZKP to verify FHE computations would be extremely computationally intensive, making it almost impossible to achieve at the current technological stage.
Fhenix has recently collaborated with EigenLayer to develop FHE coprocessors, introducing FHE computations into other public chains, L2s, and L3s. Since Fhenix employs fraud proofs with a 7-day challenge period, EigenLayer's services can help coprocessors achieve fast transaction confirmations, significantly improving performance.
Inco is a modular confidential computing Layer 1 that can serve as a universal confidentiality layer for Web3. It supports fhEVM, enabling developers to rapidly build confidential DApps using the Solidity language and development tools within the Ethereum ecosystem. Additionally, Inco provides Confidentiality as a Service (CaaS) to EVM and Cosmos chains lacking native encryption through bridging and the IBC protocol. The CaaS service primarily offers three functionalities:
On-chain encrypted state: Directly store encrypted data on-chain without off-chain storage.
Composable encrypted state: Perform state transitions on encrypted data entirely on-chain without decryption.
On-chain randomness: Generate randomness for applications on-chain without the need for external randomness services, allowing for direct on-chain application development.
Currently, Inco has several use cases, including gaming, NFTs, RWA, voting governance, and DID.
Mind Network is the first FHE Restaking Layer tailored specifically for AI and PoS networks. As a Restaking Layer, it accepts restaking tokens from ETH, BTC, and AI bluechips for staking. Simultaneously, as an FHE validation network, it utilizes FHE technology to validate node data and reach consensus, ensuring data integrity and security. Mind Network provides economic security guarantees for decentralized AI, DePIN, EigenLayer AVS, Babylon AVS, and critical PoS networks, maintaining the overall system's consensus and trustworthiness.
Restaking Layer: Collaborating with EigenLayer, StakeStone, Renzo, Babylon, Ankr, and others, Mind Network derives security from the Ethereum and Bitcoin networks. It also collaborates with Chainlink CCIP, Connext, and others to enable cross-chain remote restaking.
Security Layer: Mind Network introduces FHE-enhanced validators to ensure end-to-end encryption during the validation and consensus computation processes, and further enhance security by integrating the modular fhEVM of Fhenix and Inco.
Consensus Layer: Mind Network introduces a Proof of Intelligence (PoI) consensus mechanism designed specifically for AI tasks, ensuring fair and secure reward distribution among FHE validators. Additionally, Mind Network is collaborating with AltLayer, EigenDA, and Arbitrum Orbit to launch a Rollup chain, enhancing consensus computation with lower costs and faster performance.
Privasea is a DePIN+AI network for FHE machine learning, whose technical architecture includes the following core components:
HESea Library: An advanced FHE library that provides secure computation capabilities on encrypted data. This library supports various FHE schemes such as TFHE, CKKS, BGV/BFV, and others.
Privasea API: The application programming interface for the Privasea AI network, offering a suite of functionalities and endpoints to simplify data submission, model training, and prediction operations, while ensuring data encryption during transmission and processing.
Privanetix: A decentralized computation network composed of multiple high-performance computing nodes capable of efficiently processing encrypted data. Each node integrates the HESea library to ensure data privacy and computational performance.
Privasea Smart Contract Suite: A blockchain-based incentive mechanism that tracks the registration and contributions of Privanetix nodes through smart contracts, validates computations, and distributes rewards, ensuring participant engagement and fairness.
Privasea has launched the ImHuman application, which develops a Proof of Human based on FHE, aiming to prove that users are human beings to protect their digital identities from impersonation by bots and artificial intelligence. Users can verify their human identity through facial biometric recognition technology, generating a unique NFT as proof of their individual human identity. With the ImHuman application, users can securely attest their personal identities on Web3 and Web2 platforms without revealing personal details.
In the DeFi domain, FHE enables transactions and fund flows to take place without disclosing sensitive financial information, thereby protecting user privacy and reducing market risks. It may also serve as one of the effective solutions to the MEV problem. For fully on-chain games, FHE ensures that player scores, game progress, and other data are protected in an encrypted state, while simultaneously allowing game logic to be executed on-chain without exposing the data, enhancing fairness and security of the games. In the AI field, FHE allows for the analysis and model training on encrypted data, not only protecting data privacy but also promoting cross-institutional data sharing and collaboration, driving the development of more secure and compliant AI applications.
While FHE still faces numerous challenges in practicality and efficiency, its unique theoretical foundation brings hope for overcoming these bottlenecks. In the future, FHE is expected to significantly enhance performance and expand application use cases through algorithm optimization, hardware acceleration, and other approaches, providing a more robust foundation for data privacy protection and secure computation.
https://github.com/zama-ai/tfhe-rs
https://github.com/zama-ai/concrete
https://github.com/zama-ai/concrete-ml
https://github.com/zama-ai/fhevm
https://docs.mindnetwork.xyz/minddocs
https://drive.google.com/file/d/1jbxWMgEziupt119gvM1n0Mu8sDdM7VWF/view
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