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.

• Type: Conference
• Year: 2010
• Where: ICDT
• Authors: Wen Ming Liu, Lingyu Wang, Lei Zhang
• Keywords: Adversarial Inferences, Data Utility, ICDT 2010, K-jump Algorithms