An Analytical Study on Privacy Preserving Data Publishing using Generalization and Suppression

Authors

  • Meeta B. Fadnavis  Lecturer, Department of Computer Management Dharampeth Polytechnic, Nagpur, Maharashtra, India

Keywords:

Data Publishing, Privacy Preserving, Anonymity Algorithms, Information Metric, Generalization, Suppression

Abstract

These days, information sharing as an imperative part shows up in our vision, realizing a mass of dialogs about strategies and systems of privacy preserving data publishing which are viewed as solid assurance to keep away from information exposure and ensure people's privacy. Late work concentrates on proposing diverse anonymity calculations for shifting data publishing situations to fulfill privacy prerequisites, and keep data utility in the meantime. K-anonymity has been proposed for privacy preserving data publishing, which can avoid linkage attacks by the methods for anonymity operation, for example, generalization and suppression. Various anonymity calculations have been used for accomplishing k-anonymity. This paper gives an outline of the advancement of privacy preserving data publishing, which is confined to the extent of anonymity calculations utilizing generalization and suppression. The privacy preserving models for attack is presented at first. A diagram of a few anonymity operations take after behind. The most vital part is the scope of anonymity calculations and information metric which is fundamental element of calculations. The conclusion and point of view are proposed at long last.

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Published

2018-02-28

Issue

Section

Research Articles

How to Cite

[1]
Meeta B. Fadnavis, " An Analytical Study on Privacy Preserving Data Publishing using Generalization and Suppression, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 2, pp.108-115, January-February-2018.