The mystery of data sharing and privacy protection: what is secure multi-party computation?
As an intangible asset in today’s society, data is greatly undervalued because of the poor liquidity of data. There are three main reasons: the “information isolation”, serious privacy terms and frequent data breach. How to share private data within protecting privacy is an important problem to be solved in data value release. Secure multi-party computation is an efficient technology to solve the above problems.
Secure muti-party Computation (MPC) is a subfield of cryptography with the goal of creating methods for parties to jointly compute a function over their inputs while keeping those inputs private. It was first proposed by computer scientist Professor Andrew Yao in 1982. With the increasing demand for privacy data sharing, MPC has attracted great attention in the field of cryptography and has developed into a key technology to solve various privacy protection problems. This technology is also one of the most important underlying technologies to achieve NFT privacy protection and sharing on the GoodData blockchain platform.
How does secure muti-party computation achieve private data sharing? The following four technologies are the major schemes for MPC:
Homomorphic encryption is a form of encryption that allows users to perform computations on its encrypted data without decrypting it. The result is the same as that obtained by processing the unencrypted original data in the same method.
Its core technology is to compile the multi-party operation task into the form of Boolean circuit, then encrypt and decrypt the output entry of the truth table with the corresponding two input labels, so as to achieve the normal output of the circuit without divulging the private information of both parties in the calculation, ensuring the privacy and security of users to a great extent.
Oblivious transfer protocol is a secure two-party computation protocol. In this protocol, the sender transfers one of potentially many pieces of information to a receiver, but remains oblivious as to what piece (if any) has been transferred.
Secret sharing refers to methods for distributing a secret among a group of participants, each of whom is allocated a share of the secret. The secret can be reconstructed only when a sufficient number, of possibly different types, of shares are combined; individual shares are of no use on their own.
The usage of MPC is especially important in blockchain networks. The advantages of blockchain such as decentralization, no-tampering and traceability, combined with the input privacy protection and calculation accuracy of secure multi-party computation, form the comprehensive characteristics of decentralization, data protection and joint computing, which can provide technical support for a wide range of data encryption computation applications.
By utilizing the secret sharing and homomorphic encryption of MPC, GoodData blockchain has built a NFT as a service blockchain ecosystem. At present, GoodData has a number of upcoming blockchain technology innovations, including core technologies and applications such as machine learning, federated learning, secure multi-party computation, blockchain consensus scheme, etc.
With data value mining as the core and based on technologies such as blockchain, smart contract, secret sharing and homomorphic encryption algorithm, GoodData can help enterprises or institutions to utilize data from all parties for secure collaborative computation under encryption conditions on the premise of ensuring their own data privacy and security.
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