Secure multiparty computation

In cryptography, secure multiparty computation is a problem that was initially suggested by Andrew C. Yao in a 1982 paper. In that publication, the millionaire problem was introduced: Alice and Bob are two millionaires who want to know which is richer, but without revealing the precise amount of their wealth. Yao proposed a solution allowing Alice and Bob to satisfy their curiosity while respecting the constraints. This problem and result gave way to a generalization called multiparty computation (MPC) protocols. In a MPC, we have a given number of participants p1, p2, ..., pN, each having a private data, respectively d1, d2, ..., dN. The participants want to compute the value of a public function F on N variables at the point (d1, d2, ..., dN). A MPC protocol is dubbed secure if no participant can learn more than what he/she can learn from his/her own entry, the description of the public function and the result of the global calculation, under particular conditions depending of the model used.

Like many cryptographic protocols, the security of a MPC protocol can be computational (i.e. based on some mathematical problem, like factoring) or unconditional (usually with some probability of error which can be made arbitrarily small). The model in which the scheme is described might assume that participants use a synchronised network (a message sent at a "tick" always arrives at the next "tick"), that a secure and reliable broadcast channel exists, that a secure communication channel exists between every pair of participants (an adversary cannot read, modify or generate messages in the channel), etc... The centrally controlled adversary considered can be passive (only allowed to read the data of a certain number of participants) or active (can corrupt the execution protocol or a certain number of participants). An adversary can be static (chooses its victims before the start of the multiparty computation) or dynamic (can chose its victims during the course of execution of the multiparty computation). Attaining security against a dynamic adversary is often much harder than security against a static adversary. An adversary can be defined as a threshold structure (meaning that it can corrupt or simply read the memory of a number of participants up to some threshold), or be defined as a more complex structure (it can affect certain predefined subsets of participants, modeling different possible collusions).

Secure multiparty computations are closely related to the problem of secret sharing, and more specifically verifiable secret sharing (VSS), every MPC protocol uses a VSS.

Secure MPC provide solutions to various real-life problems such as distributed voting, private bidding and auctions, sharing of signature or decryption functions, private information retrieval, etc.

External links

• Helger Lipmaa's links about multiparty computation (http://www.cs.ut.ee/~helger/crypto/link/mpc/)
• Secure distributed CSP (DisCSP) solvers (http://tibles.cs.fit.edu/~msilaghi/secure/) — a web-application with an applet-interpreter to design and run your own full-fledged secure multiparty computation (based on the SMC declarative language). Uses secure arithmetic circuit evaluation and mix-nets.
• The Fairplay Project (http://www.cs.huji.ac.il/labs/danss/Fairplay/) — a package with a full-fledged secure two-party computation (based on a function description language). Uses secure boolean circuit evaluation.zh:安全多方计算