Toward Secure Clustered Multi-Party Computation: A Privacy-Preserving Clustering Protocol
Despite a large amount of research work has been done and a large number of results produced, the deployment of Secure Multi-party Computation (SMC) protocols for solving practical problems in real world scenarios is still an issue. This is mainly due to the complexity of the SMC-based solutions and to the needed assumptions that are not easy to fit to the considered problem. In this paper we propose an innovative approach for the deployment of SMC, providing a tradeoff between efficiency and privacy. In the Secure Clustered Multi-Party Computation (SCMC) approach, a function is more efficiently computed through reducing the number of participants to the SMC protocol by clustering, such that a reasonable privacy leakage inside the cluster is allowed. Toward this direction, this paper verifies the impact and the feasibility of applying different clustering techniques over the participants to a SMC protocol and proposes an effective specifically-tailored clustering protocol.
KeywordsSecure Multi-Party Computation Privacy-Preserving Clustering Privacy and Efficiency Tradeoff
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