面向互联网开放平台保护用户隐私的关联规则挖掘算法

摘要

互联网开放平台提供的用户信息授权服务得到了广泛应用，但其在满足第三方网站的数据挖掘需求时往往将用户隐私信息交由多方存储，因而加重用户隐私滥用与泄露风险。针对这一问题，提出了一种开放平台与网站间的分布式关联规则挖掘算法，算法无须可信第三方参与，双方各自依据挖掘条件生成以频繁-1项集编号、用户身份标志符为行、列标记的布尔型矩阵，由开放平台进行矩阵扰动和整合，再由网站在整合矩阵上挖掘全局关联规则。实验证明该算法有效，且没有因通信代价而显著降低挖掘时间效率。%In order to meet the demands of third-party websites in data mining process,the personal data authentication servi-ces provided by Internet open platform might add to security risk by storing privacy sensitive information on various websites. This paper proposed a distributed association rules mining algorithm for internet open platform and third-party website.The al-gorithm had no need for a trusted third-party,the two parties generate matrixes with the number of frequent-1 item set and user identification as markings of rows and columns based on mining conditions respectively,the matrixes of two parties were dis-rupted randomly and integrated by internet open platform.Website could generate global association rules based on the inte-grated matrix.Experiment shows that the algorithm is effective,and the mining efficiency is not significantly influenced by communication cost.