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- W2099254566 abstract "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." @default.
- W2099254566 created "2016-06-24" @default.
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- W2099254566 date "2009-01-01" @default.
- W2099254566 modified "2023-09-26" @default.
- W2099254566 title "Three New Approaches to Privacy-preserving Add to Multiply Protocol and its Application" @default.
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- W2099254566 doi "https://doi.org/10.1109/wkdd.2009.34" @default.
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