%0 Journal Article
%T Privacy Preserving Distributed Data Mining Based on Game Theory<br>基于博弈论的隐私保护分布式数据挖掘
%A GE Xin-jing
%A ZHU Jian-ming
%A <br>葛新景
%A 朱建明
%J 计算机科学
%D 2011
%I 
%X 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.
%K Game theory
%K Privacy-preserving
%K Distributed data mining<br>博弈论，隐私保护，分布式数据挖掘
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=17DDCED190714E79973DA44E367BA8F4&yid=9377ED8094509821&vid=16D8618C6164A3ED&iid=708DD6B15D2464E8&sid=8575BEDA702C4B7C&eid=43608FD2E15CD61B&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=0