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Article type: Research Article
Authors: Liu, Wen Minga; * | Wang, Lingyua | Zhang, Leib | Zhu, Shunzhic
Affiliations: [a] Concordia University, Montreal, QC, Canada. E-mails: l_wenmin@ciise.concordia.ca, wang@ciise.concordia.ca | [b] Google Inc., New York, NY, USA. E-mail: leo.zhang@gmail.com | [c] Xiamen University of Technology, Xiamen, China. E-mail: szzhu@xmut.edu.cn
Correspondence: [*] Corresponding author: Wen Ming Liu, Concordia University, Montreal, QC, Canada, H3G 1M8. Tel.: +1 514 848 2424 5642; Fax: +1 514 848 3171; E-mail: l_wenmin@ciise.concordia.ca.
Abstract: Data owners are expected to disclose micro-data for research, analysis, and various other purposes. In disclosing micro-data with sensitive attributes, the goal is usually two fold. First, the data utility of disclosed data should be maximized for analysis purposes. Second, the private information contained in such data must be to an acceptable level. Typically, a disclosure algorithm evaluates potential generalization functions in a predetermined order, and then discloses the first generalization that satisfies the desired privacy property. Recent studies show that adversarial inferences using knowledge about such disclosure algorithms can usually render the algorithm unsafe. In this paper, we show that an existing unsafe algorithm can be transformed into a large family of safe algorithms, namely, k-jump algorithms. We then prove that the data utility of different k-jump algorithms is generally incomparable. The comparison of data utility is independent of utility measures and syntactic privacy models. Finally, we analyze the computational complexity of k-jump algorithms, and confirm the necessity of safe algorithms even when a secret choice is made among algorithms.
Keywords: Privacy preservation, micro-data disclosure, generalization
DOI: 10.3233/JCS-140514
Journal: Journal of Computer Security, vol. 23, no. 2, pp. 131-165, 2015
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