Concretely efficient secure multi-party computation protocols: survey and more

Abstract

Secure multi-party computation (MPC) allows a set of parties to jointly compute a function on their private inputs, and reveals nothing but the output of the function. In the last decade, MPC has rapidly moved from a purely theoretical study to an object of practical interest, with a growing interest in practical applications such as privacy-preserving machine learning (PPML). In this paper, we comprehensively survey existing work on concretely efficient MPC protocols with both semi-honest and malicious security, in both dishonest-majority and honest-majority settings. We focus on considering the notion of security with abort, meaning that corrupted parties could prevent honest parties from receiving output after they receive output. We present high-level ideas of the basic and key approaches for designing different styles of MPC protocols and the crucial building blocks of MPC. For MPC applications, we compare the known PPML protocols built on MPC, and describe the efficiency of private inference and training for the state-of-the-art PPML protocols. Furthermore, we summarize several challenges and open problems to break though the efficiency of MPC protocols as well as some interesting future work that is worth being addressed. This survey aims to provide the recent development and key approaches of MPC to researchers, who are interested in knowing, improving, and applying concretely efficient MPC protocols.

Post‐quantum secure two‐party computing protocols against malicious adversaries

“…The simulator  sends y = {y 1 , … , y s } to a trusted third party that computes f and receives its returned output value z. 4. The simulator  generates the accurate GC for each j ∈  in the same manner as how an honest P 1 would construct the circuit.…”

“…It is defined as multiple parties conducting joint computation on their respective inputs securely, and no additional information can be obtained by any party except for the final output and information that can be deduced from the output. 3,4 Yao 5,6 proposed the first traditional secure two-party computation (S2PC) protocol in the 1980s. It was based on oblivious transfer (OT) and garbled circuits (GC).…”

“…In addition, Secure multi‐party computation protocol is a feasible tool. It is defined as multiple parties conducting joint computation on their respective inputs securely, and no additional information can be obtained by any party except for the final output and information that can be deduced from the output 3,4 …”

Two‐Server Oblivious Transfer for Quantum Messages

“…It is known that oblivious transfer is an essential building block for secure multiparty computation. [33,34] In secure multiparty computation, it is natural to restrict the number of colluding players. Hence, two-server oblivious transfer is helpful for secure multiparty computation.…”

Secure Multi-Party Computation for Machine Learning: A Survey

“…The research contributions of Wang et al [9] and Feng et al [10] are noteworthy for their comprehensive review on secure multi-party computation protocols and techniques. Wang et al [9] thoroughly discuss the classification of adversaries within the context of SMPC, suggesting that contemporary adversaries may be overestimated in their capabilities.…”