Many mechanisms exist in today’s society that can handle trust relations between units. Some technological means are commonly being developed based on cryptographic protocols that involve cooperation between multiple units, so that no single unit can learn more than it is allowed. The aspect of the trust of hardware in such settings of cooperation is the main subject of the hardware trust section of this work. To do this, we will provide a general introduction to secure multi-party computation and we will then sketch some of the approaches taken to employ the principles of secure computation for the special case of networks of decentralized physical nodes.
There are various definitions for secure computation, and for secure multi-party computation in particular. Several definitions have been suggested and it was subsequently shown that these were equivalent. This made some believe that the actual definition of security was not important and the focus has shifted towards the development of efficient protocols that can provide security according to these definitions. Some authors use different terminologies with slightly matching definitions and similar flavors. As shown in earlier works, it is often advantageous to use physical means as substrates to provide security. When these physical means can be used in cooperation, decentralized physical networks are described.
Research and development related to DEPIN often employs efficient models and simulation for providing ad-hoc statistical analyses or improvements. Resulting approaches to the assessment of DEPINs are often centralized. SMPC is one of the main enabling technologies that effectively decentralize the information and decision-making within the network. Methods of providing security are partially operational: hardware root of trust at the trusted platform module (TPM), an alternative root of trust in “secure” CPU, secure firmware. Encryption adopters are showing a significant increase and private organizations are adopting encryption-based solutions.
Secure Multi-party Computation (SMPC) is a subfield of cryptographic tools that enables two or more members to store and operate data whose legitimate features remain hidden from any other participants. Higher-level security guarantees are achieved by relaxing the assumption on any central authority by means of encrypting data, which is the fundamental philosophy of SMPC. Technically, in SMPC, a large amount of data is conveyed and operated over encrypted data points, and the results are also hashed in a way that neither of the participants shares access to plain data with full opacity. Consequently, SMPC could play an essential role in guarding privacy rights in the era of big data, and they are used in various academic and corporate activities where privileged data is exchanged between members. Examples of these activities are privacy-preserving computation, electronic auctions and contract signing, electronic voting, secure electronic payment, distributed database execution, secure internet access, secure communication, and secure social events. Furthermore, the SMPC platforms are also extended and integrated with blockchain technologies and provide a new decentralized network imagery, fundamentally changing many societal norms like diplomacy rules and rules of sports.
Generally, SMPC research focuses on many basic privacy and functionality properties where security can be evaluated in terms of completeness, soundness, computational complexity, noise tolerance, ability to resist different attacks, performance in terms of time and space, and reusability in different technologies with several implementations. In this paper, we will give a brief on how to integrate SMPC techniques within DEPIN. Technically, two or more peer devices are involved in operations for a task. Each device can work with encrypted data within the task, and the task is exclusive to a single device.
Secure Multi-party Computation (SMPC) is a relatively recent field of study that has grown exponentially in the past decade. It consists of protocols that enable mutually distrusting parties to execute a joint computation on their private inputs. SMPC predicates on the notion of “active” and “passive” adversaries. Whereas in the real world, active adversaries are parties that may compromise other parties and act on their behalf, in the formal setting they can be envisioned as operating in direct connection with the participants. Protocols that resist a malicious or active adversary over an indefinite number of rounds are referred to as “multiparty secure computation” protocols, whereas those resilient against “passive” adversaries are referred to as “secure function evaluation” protocols. While SMPC appears to be quite a theoretical concept, it, in fact, caters to a real-world need for digitalized societies, offering solutions for decentralized or privacy-preserving computations that can scale.
SMPC can be challenging to understand, hence the purpose of this article . After establishing the principles underpinning SMPC, the current scope of uses and applications for SMPC are pointed out before launching into the core of this thesis. The objective is to leave a clear indication of the current practical limitations and challenges of SMPC (to be discussed in the next article). An integrative view on the fundamental concepts underpinning DEPIN is given once all these concepts have been independently explained. Examples of real-world DEPIN implementations from the standpoint are given below to illustrate such a sophisticated view.
In recent years, secure multi-party computation (SMPC) has been explored in a variety of practical applications, particularly in the fields of decentralization, real-time computation, optimized decision-making, permissioned blockchains, and cryptocurrencies. Highly sensitive and mission-critical data are stored with encryption, and then SMPC protocols are used over those datasets to obtain unencrypted results by all parties. Such results will be stored directly or utilized in any other secure isolation mechanism. The smart contract by Ethereum is an example application of SMPC for permissioned blockchains, where most of the existing works use various specially built zero-knowledge proof-based smart contracts to perform a secret contract between the organizations.
The privacy of untrusted organizations participating in permissionless cryptocurrency blockchains is becoming the driving factor behind the development of SMPC, FSS, and MPC. Then, the existing blockchain helps the two overlay networks pass messages using specialized BTC or ETH. In Bitcoin/Ethereum, zero-knowledge proof is used as a specially built smart contract for each type and used for consensus management. It also ran a dedicated EVM for the compilation and deployment of each smart contract, a very complex approach. Furthermore, it fails to provide support for real-world applications running between diverse devices. Critical services are provided to vendors and deployed in dangerous environments with threats to be compromised by hackers or competitors.