An overview of Multiplicative data perturbation for privacy preserving Data mining
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 7
Abstract
Privacy is an important issue when one wants to make use of data that involves individuals’ sensitive information. Research on protecting the privacy of individuals and the confidentiality of data has received contributions from many fields, including computer science, statistics, economics, and social science. In this paper, we survey research work in privacypreserving data Mining. This is an area that attempts to answer the problem of how an organization, such as a hospital, government agency, or insurance company, can release data to the public without violating the confidentiality of personal information. We focus on privacy criteria that provide formal safety guarantees, present algorithms that sanitize data to make it safe for release while preserving useful information, and discuss ways of analyzing the sanitized data. Many challenges still remain. This overview provides a summary of the current and traditional multiplicative data perturbation techniques for privacy preserving Data Mining.
Authors and Affiliations
Keerti Dixit, Bhupendra Pandya
Keywords
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