DAS(database-as-a-service)模型是最近出现的一种新的数据管理模型,它把用户的数据存放在数据库服务提供端(database service provider,DSP)并让它们通过网络使用数据库管理系统,因此这种模型对外购数据库的安全性提出了更高的要求:不仅可以防止外部攻击者对重要数据的窃取或篡改,而且可以防止DSP内部人员的非法访问.加密技术是确保外购数据安全的基本手段.提出了一种简单有效的基于DAS模型的数据库加密方法,并分析了这种加密方法的优缺点;同时还设计了密文数据上的查询,提出了有待进一步解决的问题.
In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users’ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.
