Privacy Preserving Distributed Classification: A Satisfaction Equilibrium Approach

The privacy issue arising in data mining applications has attracted much attention in recent years. In the context of distributed data mining, the participant can employ data perturbation techniques to protect its privacy. Data perturbation generally causes a negative effect on the mining result, which means there is a trade-off between privacy and the mining result. In this paper, we study a distributed classification scenario where a number of users provide data to a mediator to train a classifier. 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. The basis idea is that the user gradually reduces the perturbation in data until it is satisfied with the classification accuracy. Experimental results based on real data 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 classification accuracy and the preserved privacy.