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k-jump strategy for preserving privacy in micro-data disclosure

8 years 8 months ago
k-jump strategy for preserving privacy in micro-data disclosure
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 limited to an acceptable level. Recent studies show that adversarial inferences using knowledge about a disclosure algorithm can usually render the algorithm unsafe. In this paper, we show that an existing unsafe algorithm can be transformed into a large family of distinct safe algorithms, namely, k-jump algorithms. We prove that the data utility of different k-jump algorithms is generally incomparable. Therefore, a secret choice can be made among all k-jump algorithms to eliminate adversarial inferences while improving the data utility of disclosed micro-data.
Wen Ming Liu, Lingyu Wang, Lei Zhang
Added 19 Jul 2010
Updated 19 Jul 2010
Type Conference
Year 2010
Where ICDT
Authors Wen Ming Liu, Lingyu Wang, Lei Zhang
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