Secure Association Rule Mining on Vertically Partitioned Data Using Private-Set Intersection
نویسندگان
چکیده
منابع مشابه
Privacy Preserving Association Rule Mining in Vertically Partitioned Data
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 eit...
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There has been concern over the apparent conflict between privacy and data mining. There is no inherent conflict, as most types of data mining produce summary results that do not reveal information about individuals. The process of data mining may use private data, leading to the potential for privacy breaches. Secure Multiparty Computation shows that results can be produced without revealing t...
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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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Data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. It transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved ag...
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In today’s ever-increasingly digital world, the concept of data privacy has become more and more important. Researchers have developed many privacy-preserving technologies, particularly in the area of data mining and data sharing. These technologies can compute exact data mining models from private data without revealing private data, but are generally slow. We therefore present a framework for...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3014330