Multi-Party Computation (MPC)
Multi-Party Computation (MPC) is a subfield of cryptography that enables multiple parties to jointly compute a function over their inputs while keeping those inputs private. The core idea is that each participant can contribute their data to the computation without revealing their data to the other participants, ensuring data privacy and security throughout the process.
Key Concepts of Multi-Party Computation (MPC)
Privacy: MPC allows parties to keep their inputs confidential. No party learns anything about the other parties' inputs except what can be inferred from the final output.
Correctness: The computation is performed correctly according to the agreed-upon function, even if some participants are dishonest or untrusted.
Security Models:
Semi-Honest Model: Assumes that all parties follow the protocol correctly but may try to glean information from received messages.
Malicious Model: Assumes that parties may deviate from the protocol in any way, including sending incorrect messages, in an attempt to learn others' inputs or disrupt the computation.
Applications:
Secure Voting: Enables secure and private electronic voting systems where votes are confidential but counted correctly.
Privacy-Preserving Data Analysis: Allows multiple organizations to compute joint analytics without sharing their proprietary data.
Financial Services: Used in secure auctions, joint risk analysis, and privacy-preserving financial computations between banks or financial institutions.
Machine Learning: Facilitates collaborative machine learning on sensitive data without exposing the underlying data.
Common Techniques:
Secret Sharing: A method where data is split into shares distributed among parties, so the original data is reconstructed only when enough shares are combined.
Oblivious Transfer: A protocol that allows a sender to send one of many pieces of information without revealing which piece was sent.
Garbled Circuits: A technique used to encode the function to be computed in a way that prevents participants from learning anything beyond their input and the output.
History and Applications of MPC
MPC’s (multi-party computation) initial development began in the ’80s – a fairly recent breakthrough within the world of cryptography.
Up until that point, the majority of cryptography had been about concealing content; this new type of computation focused instead on concealing partial information while computing with data from multiple sources.
1982 – Secure two-party computation is formally introduced as a method of solving The Millionaire’s Problem
1986 – Andrew Yao adapts two-party computation to any feasible computation
1987 – Goldreich, Micali, and Wigderson adapt the two-party case to multi-party
1990s – Study of MPC leads to breakthroughs in areas including universal composability (pioneered by Fireblocks cryptography advisor Ran Canetti) and mobile security
2008 – The first large-scale, practical application of multi-party computation – demonstrated in an auction – takes place in Denmark
Late 2010s – MPC is first utilized by digital asset custodians and wallets for digital asset security
2019 – Debut of MPC-CMP, the first 1-round, automatic key-refreshing MPC algorithm
Today, MPC is utilized for a number of practical applications, such as electronic voting, digital auctions, and privacy-centric data mining. One of the top applications for multi-party computation is for securing digital assets – and recently, MPC has become the standard for institutions looking to secure their assets while retaining fast and easy access to them.
Advantages of MPC
Enhanced Privacy: Protects sensitive data by ensuring inputs remain private throughout the computation.
Security Against Collusion: Prevents any subset of parties from gaining unauthorized access to others' data.
Decentralization: Removes the need for a trusted third party to perform secure computations.
Challenges and Disadvantages
Computational Complexity: MPC protocols can be computationally intensive, especially as the number of participants or the complexity of the function increases.
Communication Overhead: Requires significant communication between parties, which can be slow and bandwidth-intensive.
Scalability: Scaling MPC to large numbers of participants or highly complex computations remains challenging.
MPC is a powerful cryptographic tool that allows secure collaborative computation without compromising individual privacy. Its applications are increasingly relevant in a world where data privacy is paramount, particularly in sectors like finance, healthcare, and data analytics. However, the complexity and computational demands of MPC mean it requires careful implementation and optimization to be practical for large-scale use.
Last updated