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Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing

Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing

Sowmyarani C. N., Dayananda P.
Copyright: © 2019 |Pages: 21
ISBN13: 9781522588979|ISBN10: 1522588973|EISBN13: 9781522588986
DOI: 10.4018/978-1-5225-8897-9.ch076
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MLA

Sowmyarani C. N., and Dayananda P. "Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing." Cyber Law, Privacy, and Security: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2019, pp. 1518-1538. https://doi.org/10.4018/978-1-5225-8897-9.ch076

APA

Sowmyarani C. N. & Dayananda P. (2019). Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing. In I. Management Association (Ed.), Cyber Law, Privacy, and Security: Concepts, Methodologies, Tools, and Applications (pp. 1518-1538). IGI Global. https://doi.org/10.4018/978-1-5225-8897-9.ch076

Chicago

Sowmyarani C. N., and Dayananda P. "Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing." In Cyber Law, Privacy, and Security: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1518-1538. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8897-9.ch076

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Abstract

Privacy attack on individual records has great concern in privacy preserving data publishing. When an intruder who is interested to know the private information of particular person of his interest, will acquire background knowledge about the person. This background knowledge may be gained though publicly available information such as Voter's id or through social networks. Combining this background information with published data; intruder may get the private information causing a privacy attack of that person. There are many privacy attack models. Most popular attack models are discussed in this chapter. The study of these attack models plays a significant role towards the invention of robust Privacy preserving models.

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