分布式隐私保护数据挖掘研究*

隐私保护挖掘是近年来数据挖掘领域的热点之一，主要研究在避免敏感数据泄露的同时在数据中挖掘出潜在的知识。实际应用中，大量的数据分别存放在多个站点，因此分布式隐私保护数据挖掘(distributed privacy preserving data mining, DPPDM)的研究更具有实际意义。对该领域的研究进行了详细的阐述，比较了各种方法的优缺点，对现有方法进行了分类和总结，最后指出了该领域未来的研究方向。

State-of-the-art in distributed privacy preserving data mining

In recent years, privacy preserving data mining is one of the hot point problems in data mining. The chief research is how to mine the potential knowledge and not to reveal the sensitive data. In reality, large amounts of data stored in multiple sites, so the DPPDM (distributed privacy preserving data mining) is more important. This paper summarized the features of DPPDM, detailed described the research in this area, compared the advantages and disadvantages of each method, surveyed the state-of-the-art in DPPDM. Furthermore, it pointed out the future research directions.