OptRR: Optimizing Randomized Response Schemes for Privacy-Preserving Data Mining

The randomized response (RR) technique is a promising technique to disguise private categorical data in privacy-preserving data mining (PPDM). Although a number of RR-based methods have been proposed for various data mining computations, no study has systematically compared them to find optimal RR schemes. The difficulty of comparison lies in the fact that to compare two PPDM schemes, one needs to consider two conflicting metrics: privacy and utility. An optimal scheme based on one metric is usually the worst based on the other metric. In this paper, we first describe a method to quantify privacy and utility. We formulate the quantification as estimate problems, and use estimate theories to derive quantification. We then use an evolutionary multi-objective optimization method to find optimal disguise matrices for the randomized response technique. The experimental results have shown that our scheme has a much better performance than the existing RR schemes.