On privacy preference in collusion-deterrence games for secure multi-party computation

Abstract

Secure multi-party computation (MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. Information-theoretic secure MPC protocols, though more efficient than their computationally secure counterparts, require at least three computational parties and are prone to collusion attacks. Previous work has used mechanism designs to deter collusion. An important element missing is the consideration of how different players value privacy. In this paper, we provide a detailed analysis of possible outcomes under different privacy preferences based on the relative cost of collusion attacks over loss of privacy. We explicitly calculate the conditions under which honesty is the solution. Simulation results provide further evidence to demonstrate the validity of our mechanism design.

Secure Data Collaborative Computing Scheme Based on Blockchain

“…rough the performance analysis, we compare and analyze the existing SMPC based on blockchain. In order to prevent collusion, Wang and Sen-ching and Luo and Li [4,23] improved the use mechanism of previous work. e influence of privacy preference on preventing collusion attack is analyzed.…”

“…At present, in the research of SMPC, literatures [3,4] focus on how to prevent collusion in multiparty computing, focusing on anticollusion but not paying attention to privacy protection. At the same time, there are some problems in SMPC, such as frequent interaction between participants, which reduces the efficiency, and only part of the output of participants cannot achieve fairness.…”