Privacy-Accuracy Trade-Off in Distributed Data Mining

Abstract

An important issue in distributed data mining is privacy. It is necessary for each participant to make sure that its privacy is not disclosed to other participants or a third party. To protect privacy, one can apply a differential privacy approach to perturb the data before sharing them with others, which generally hurts the mining result. That is to say, the participant faces a trade-off between privacy and the mining result. In this chapter, we study a distributed classification scenario where a mediator builds a classifier based on the perturbed query results returned by a number of users. A game theoretical approach is proposed to analyze how users choose their privacy budgets. Specifically, interactions among users are modeled as a game in satisfaction form. And an algorithm is proposed for users to learn the satisfaction equilibrium (SE) of the game. Experimental results demonstrate that, when the differences among users’ expectations are not significant, the proposed learning algorithm can converge to an SE, at which every user achieves a balance between the accuracy of the classifier and the preserved privacy.