A Modified Algorithm for Privacy Preserving Association Rule Hiding

Manish Vyas, Vikas Choudhary


Mining association rules from huge amounts of data is an important issue in data mining, with the discovered information often being
commercially valuable. Moreover, companies that conduct similar business are often willing to collaborate with each other by mining
significant knowledge patterns from the collaborative datasets to gain the mutual benefit. However, in a cooperative project, some of
these companies may want certain strategic or private data called sensitive patterns not to be published in the database. Therefore,
before the database is released for sharing, some sensitive patterns have to be hidden in the database because of privacy or security
concerns. To solve this problem, sensitive-knowledge-hiding (association rules hiding) problem has been discussed in the research
community working on security and knowledge discovery. The aim of these algorithms is to extract as much as non sensitive
knowledge from the collaborative databases as possible while protecting sensitive information. Sensitive-knowledge-hiding problem
was proven to be a nondeterministic polynomial-time hard problem. After that, a lot of research has been completed to solve the
problem. In this article, we will introduce a new modified hybrid algorithm for privacy preserving.
There are many approaches to hide certain association rules which take the support and confidence as a base for algorithms ([1, 2,
6,7]). Our approach is a modification of work done by [7]. Our algorithm takes lesser number of passes to hide a specific association


Association Rule Mining, Sensitive Rule Hiding, Support, Confidence

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