Three New Approaches to Privacy-preserving Add to Multiply Protocol and its Application
Youwen Zhu, Liusheng Huang, Wei Yang, Dong Li, Yonglong Luo, Fan Dong · 2009
Privacy-preserving Data Mining aims at securely extracting knowledge from two or more parties' private data. Secure Multi-party Computation is the paramount approach to it. In this paper, we study Privacy-preserving Add and Multiply Exchanging Technology and present three new different approaches to Privacy-preserving Add to Multiply Protocol. After that, we analyze and compare the three different approaches about the communication overheads, the computation efforts and the security. In addition, we extend Privacy-preserving Add to Multiply Protocol to Privacy-preserving Adding to Scalar Product Protocol, which is more secure and more useful in the high security situations of Privacy-preserving Data Mining. Meantime, we present a solution for the new protocol.