Privacy Preserving Distributed Data Mining Based on Game Theory
基于博弈论的隐私保护分布式数据挖掘

Privacy preserving distributed data mining has become an important issue in the data mining. Based on economic perspectives, game theory has been applied to privacy preserving data mining, which is a relatively new area of research. This paper studied the strategies of partics(two-party or multi-party) by using a complete information static game theory framework for the privacy preserving distributed data mining, where each party tries to maximize its own utility. Research results show that the semi-honest adversary strategy of partics(two-party or multi-party) is Pareto dominance and Nash equilibrium under certain conditions in distributed data mining; and non-collusion strategy of parties(multi-party) is not a Nash equilibrium under the assumption of semi-honest adversary behavior, then the mixed strategy Nash equilibrium was given. So this paper has some theoretical and practical implication for the strategy of partics in privacy preserving distributed data mining.