k -jump: A strategy to design publicly-known algorithms for privacy preserving micro-data disclosure

Abstract

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.