Algorithm-safe privacy-preserving data publishing

This paper develops toolsets for eliminating algorithm-based disclosure from existing privacy-preserving data publishing algorithms. We first show that the space of algorithm-based disclosure is larger than previously believed and thus more prevalent and dangerous. Then, we formally define Algorithm-Safe Publishing (ASP) to model the threats from algorithm-based disclosure. To eliminate algorithmbased disclosure from existing data publishing algorithms, we propose two generic tools for revising their design: worst-case eligibility test and stratified pick-up. We demonstrate the effectiveness of our tools by using them to transform two popular existing diversity algorithms, Mondrian 1 and Hilb, to SP-Mondrian and SPHilb which are algorithm-safe. We conduct extensive experiments to demonstrate the effectiveness of SP-Mondrian and SP-Hilb in terms of data utility and efficiency.

• Type: Conference
• Year: 2010
• Where: EDBT
• Authors: Xin Jin, Nan Zhang 0004, Gautam Das
• Keywords: Algorithm-based Disclosure, Data Publishing Algorithms, Database, EDBT 2010, Privacy-preserving Data Publishing