基于SMC的隐私保护聚类模型  

Privacy-preserving clustering modeling based on SMC

作　　者：方炜炜[1,2] 杨炳儒[2] 夏红科[1,2] 

机构地区：[1]北京信息科技大学计算中心,北京100192 [2]北京科技大学信息工程学院,北京100083

出　　处：《系统工程与电子技术》2012年第7期1505-1510,共6页Systems Engineering and Electronics

基　　金：国家自然科学基金重点项目(61175048)资助课题

摘　　要：隐私保护数据挖掘指在实现准确挖掘知识的同时确保敏感数据不泄露。针对垂直分布式数据存储结构的聚类隐私保护问题,提出基于全同态加密协议和数据扰乱方法的隐私保护聚类模型。该模型通过采用安全比较协议解决了垂直分布式聚类的两个隐私保护关键步骤:求解最近簇和判断质心变化,从而实现了数据的有效保护。理论证明了该模型的安全性并分析了其时间复杂度和通信耗量,实验结果表明该隐私保护聚类模型是安全有效的。Privacy-preserving data mining aims to accurately mine knowledge while unrevealing sensitive da- ta. For solving the privacy-preserving clustering problem in vertical distribution, a privacy-preserving clustering model based on full homomorphous encryption protocols and data perturbation technology is proposed. The model protects original data effectively by using secure comparison protocols to compute the nearest cluster and estimate the updating of the cluster center, which are two key steps in clustering process. Theory argument demonstrates the security of the privacy-preserving clustering model and analyzes computation complexity and communication costs. Experiment results prove that the privacy-preserving clustering model is secure and effec- tive.

关 键 词：安全多方计算 同态加密 聚类 隐私保护数据挖掘 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]