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| Perturbation method for distributed privacy-preserving data mining |
| MA Jin, LI Feng, LI Jian-hua |
| （Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200030, China) |
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Abstract Distributed anonymous statistic mean and distributed anonymous statistic variance methods were proposed through designing randomized data separation method. A light-weight randomized data exchange protocol was proposed with homomorphic encryption mechanism to apply anonymous data exchange in distributed environment. Then a distributed anonymous data exchanging method was presented for perturbation-based privacy preserving data mining aiming at the efficiency issue towards distributed privacy preserving data mining. The experimental results and analysis show that the method is robust under high-density collusion attacks and shows more efficiency in large scale distribution environment compared with secure multiparty related methods. Furthermore, the method is flexible to apply in various types of data mining works, such as distributed associate rule mining and clustering.
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Published: 09 March 2010
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分布式数据挖掘中基于扰乱的隐私保护方法
通过设计一种随机数值片拆分统计机制,提出分布式环境下的匿名均值统计和匿名方差统计方法；结合同态加密机制,设计了分布式环境下的随机数据交换方法,实现了分布式环境中匿名数据交换机制.结合上述两种方法,提出分布式环境下基于数据扰乱技术的隐私保护方法,支持高效的分布式隐私保护数据挖掘.共谋攻击的实验结果和分析表明：匿名数据交换机制下的数据挖掘隐私保护方法在高密度共谋攻击的半诚实环境中有较好的鲁棒性,与主流的安全多方计算相比具有显著的效率优势；同时,该方法具有较高的灵活性和通用性,能应用于关联规则挖掘、聚类多种场合.
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