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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: 9781522571131|ISBN10: 1522571132|EISBN13: 9781522571148
DOI: 10.4018/978-1-5225-7113-1.ch061
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MLA

Sowmyarani C. N., and Dayananda P. "Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing." Censorship, Surveillance, and Privacy: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2019, pp. 1273-1293. https://doi.org/10.4018/978-1-5225-7113-1.ch061

APA

Sowmyarani C. N. & Dayananda P. (2019). Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing. In I. Management Association (Ed.), Censorship, Surveillance, and Privacy: Concepts, Methodologies, Tools, and Applications (pp. 1273-1293). IGI Global. https://doi.org/10.4018/978-1-5225-7113-1.ch061

Chicago

Sowmyarani C. N., and Dayananda P. "Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing." In Censorship, Surveillance, and Privacy: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1273-1293. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7113-1.ch061

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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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