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基于分布式数据的隐私保持协同过滤推荐研究 被引量：17

Research on Privacy-Preserving Collaborative Filtering Recommendation Based on Distributed Data
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摘要 针对分布式数据存储结构的协同过滤推荐隐私保持问题,以可交换的密码系统为主要技术,设计了一个协议,集中解决其核心任务——在保持用户隐私前提下对项目评分.准确度与数据集中存放一样,但能保持各分站点下用户评分数据的隐私.基于安全多方计算理论和随机预言模型,证明了协议的安全性,分析了协议的时间复杂度和通信耗费. Privacy-preserving data mining is a cutting-edge research direction in recent years. As one of its sub-directions, privacy-preserving collaborative filtering aims at protecting users＇ privacy while providing high-quality recommendations efficiently. To reserve privacy in collaborative filtering recommender systems under distributed data scenario, the core challenge how to securely rate a specific item is addressed. A protocol employing commutative encryption as its major privacy-preserving technique is introduced. traditional memory-based collaborative filtering re ratings from being known by other sites rather tha tation and random oracle model, the protocol＇s s complexity and communication costs are analyzed This protocol produces the same results as the traditional memory-based collaborative filtering recommender systems while preventing any user ratings from being known by other sites rather than by itself. Based on secure multi-party computation and random oracle model,the protocol＇s security is proved. The protocol＇s computation as well.
作者 张锋 常会友
机构地区 中山大学信息科学与技术学院
出处 《计算机学报》 EI CSCD 北大核心 2006年第8期1487-1495,共9页 Chinese Journal of Computers
基金 国家自然科学基金(60573159) 广东省自然科学基金重点项目基金(05100302)资助.
关键词 推荐系统 协同过滤 隐私保持 安全多方计算 随机预言模型 recommender system collaborative filtering privacy preserving secure multi-partycomputation random oracle model
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献23

  • 1Sarwar B. , Karypis G. , Konstan J. , Riedl J.. Analysis of recommendation algorithms for E-Commerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, Minneapolis, MN, USA, 2000, 158-167
  • 2Chee S. H. S. , Han J. W., Wang K.. RecTree: An efficient collaborative filtering method. In: Proceedings of the 3rd International Conference on Data Warehousing and Knowledge Discovery, Munich, Germany, 2001, 141-151
  • 3Kantarcioglu M. , Clifton C.. Privacy-preserving distributed mining of association rules on horizontally partitioned data.IEEE Transactions on Knowledge and Data Engineering, 2004,16(9): 1026-1037
  • 4Vaidya J. , Clifton C.. Secure set intersection cardinality with application to association rule mining. Journal of Computer Security, 2005, 13(4): 593-622
  • 5Agrawal R. , Srikant R.. Privacy-preserving data mining. In:Proceedings of the 2000 ACM SIGMOD Conference on Management of Data, Dallas, Texas, USA, 2000, 439-450
  • 6Vaidya J. , Clifton C.. Privacy-preserving k-means clustering over vertically partitioned data. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D. C. , USA, 2003, 206-215
  • 7Vaidya J. , Clifton C.. Privacy-preserving outlier detection.In: Proceedings of the 4th IEEE International Conference on Data Mining, Brighton, UK, 2004, 233-240
  • 8Wright R. , Yang Z.. Privacy-preserving Bayesian network structure computation on distributed heterogeneous data. In:Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA, 2004, 713-718
  • 9Canny J.. Collaborative filtering with privacy. In: Proceedings of the 2002 IEEE Symposium on Security and Privacy, Berkeley, California, USA, 2002, 45-57
  • 10Canny J.. Collaborative filtering with privacy via factor analysis. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland, 2002, 238-245

二级参考文献26

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计算机学报
2006年 第8期

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