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Three New Approaches to Privacy-preserving Add to Multiply Protocol and its Application

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6 Author(s)
Youwen Zhu ; Dept. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei ; Liusheng Huang ; Wei Yang ; Dong li
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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.

Published in:

Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on

Date of Conference:

23-25 Jan. 2009