摘要
电子商务网站用户的每次购物会话信息会被记录在Web服务器的日志中,分析这些日志并挖掘出购物篮商品间的强关联规则,可以主动为Web终端用户提供商品推荐,优化网站服务质量。鉴于原始用户会话信息及挖掘结果的隐私保护问题,提出了一种新的数据随机干扰处理方法,即结合列置换的伪列随机化回答方法,先对原始日志信息进行变化和隐藏,然后以此为基础,给出了一种基于位逻辑与操作的高效频繁项集生成算法,进而实现了原始信息及挖掘结果均获得隐私保护的网上购物篮问题的关联规则挖掘。实验结果表明,本方法具有很好的隐私保护性、高效准确性以及适用推广性。
Each visitor＇s shopping session of the E-Business Web site is recorded in the Web server log files. Analyzing the log files and exploring the strong regularities in the commodities of the shopping cart,can provide the recommended goods for Web users, and improve the performance of the Web service. In order to improve the privacy preservation of the original visitor＇s shopping information and mining result, an effective method for privacy preserving association rule mining was presented. First, a new data preprocessing approach, Fake Column＇s Randomized Response with Column Replacement （FCRRCR） was proposed to transform and hide the original data. Then, an effective privacy preserving association rule mining algorithm based on bit AND operation was presented. As shown in the experimental results, the algorithm can achieve significant improvements in terms of privacy, accuracy, efficiency and applicability.
出处
《计算机科学》
CSCD
北大核心
2009年第8期220-223,共4页
Computer Science
基金
国家重点基础研究发展规划(973)项目(2005CB321904)资助
关键词
WEB日志
隐私保护
关联规则
随机化回答
Web logs, Privacy preservation, Association rule, Randomized response
