Numerous privacy-preserving data publishing algorithms were proposed to achieve privacy guarantees such as ℓdiversity. Many of them, however, were recently found to be vulnerable to algorithm-based disclosure - i.e., privacy leakage incurred by an adversary who is aware of the privacy-preserving algorithm being used. This paper describes generic techniques for correcting the design of existing privacy-preserving data publishing algorithms to eliminate algorithm-based disclosure. We first show that algorithm-based disclosure is more prevalent and serious than previously studied. Then, we strictly define Algorithm-SAfe Publishing (ASAP) to capture and eliminate threats from algorithm-based disclosure. To correct the problems of existing data publishing algorithms, we propose two generic tools to be integrated in their design: global look-ahead and local look-ahead. To enhance data utility, we propose another generic tool called stratified pick-up. We demonstrate the effectiveness of our tools by applying them to several popular ℓdiversity algorithms: Mondrian, Hilb, and MASK. We conduct extensive experiments to demonstrate the effectiveness of our tools in terms of data utility and efficiency.
All Science Journal Classification (ASJC) codes
- Information Systems
- Hardware and Architecture