Efficient Collusion-Tolerable Secure Multiparty Computation of Weighted Average

Secure multiparty computation is a research focus in the international cryptographic community and a key privacy preserving technology in cyberspaces. Secure multiparty computation for the weighted average problem has great significance, and there are a wide range of applications for this problem. However, there are only a few solutions to this problem at present. In addition, to the best of our knowledge, most of the existing secure multiparty computation schemes for weighted average cannot exclude the influence of invalid data on the weighted average. To address this challenge, in this paper, we propose a corresponding solution to eliminate the influence of invalid data on the final result, and specific number of participants who give invalid datas will not be revealed. A new secure multiparty computation protocol for weighted average are designed to effectively resist different levels of collusion attacks, and the security analysis and performance analysis of the protocol is given. Finally, secure multiparty computation protocol for the weighted average problem is applied to solve secure data aggregation problem, secure weighted voting problem, and privacy-preserving location proximity detection.