Privacy Preserving Association Rule Mining in Vertically Partitioned Data
نویسنده
چکیده
Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. This paper presents privacy preserving association rule mining across vertically partitioned data. We present an efficient algorithm to discover association rules with minimum levels of support and confidence, from heterogeneous data distributed across 2 parties, while preventing either party from learning the specific data values of the other party.
منابع مشابه
Fast Steganography-based Multi-Party Protocols for Privacy-Preserving Association Rule Mining in Vertically Partitioned Data
Recently, with the emergence of privacy issues in data mining, considerable research has focused on developing new data mining algorithms that incorporate privacy constraints, and, in the same time, are as efficient as possible in terms of accuracy of the results. In this paper, we focus on privately mining association rules in vertically partitioned data, and propose two steganography-based mu...
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