Anonymization and Aggregation of Privacy Preserving of Personal data –using Slicing Technique

Abstract

  At present, most organisations are actively accumulating and storing data in large databases. Many of them have recognized in the expertise price of those data as an information source for making industry decisions. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing priceless data even as keeping data privacy. In this paper, a short but systematic evaluate of a few Anonymization systems such as generalization and Bucketization, were designed for privacy maintaining micro data publishing. Contemporary work has proven that generalization loses huge amount of understanding, mainly for prime-dimensional information. On the other hand, Bucketization does not avert membership disclosure. Where as cutting preserves better data utility than generalization and in addition prevents membership disclosure. This paper makes a speciality of potent approach that can be used for supplying better data utility and might control excessive dimensional data.