Abstract

This paper proposes a model for hiding sensitive association rules for Privacy preserving in high dimensional data. Privacy preservation is a big challenge in data mining. The protection of sensitive information becomes a critical issue when releasing data to outside parties. Association rule mining could be very useful in such situations. It could be used to identify all the possible ways by which '˜non-confidential' data can reveal '˜confidential' data, which is commonly known as '˜inference problem'. This issue is solved using Association Rule Hiding (ARH) techniques in Privacy Preserving Data Mining (PPDM). Association rule hiding aims to conceal these association rules so that no sensitive information can be mined from the database. Tata Gayathri | N Durga"Privacy Preserving Approaches for High Dimensional Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2430.pdf http://www.ijtsrd.com/engineering/computer-engineering/2430/privacy-preserving-approaches-for-high-dimensional-data/tata-gayathri

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