推荐系统中隐私增强的最优伪造和评级抑制
Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems
摘要：
推荐系统是一种信息过滤系统，它根据用户的偏好为用户定制信息。这些系统对用户进行分析的能力是实现这种智能功能的原因，但与此同时，它也是严重的隐私问题的根源。在本文中，我们研究了一种隐私增强技术，该技术旨在阻止攻击者根据他们所评价的项目准确地分析用户。我们的方法利用了两个扰动机制的组合-伪造和抑制评级。虽然这种技术在一定程度上增强了用户隐私，但它不可避免地以数据效用的损失为代价，即推荐准确性的降低。简而言之，它在隐私和实用性之间做出了权衡。本文的研究对象是对上述权衡的理论分析。我们衡量隐私的Kullback-Leibler分歧之间的用户和人口的项目分布，并量化效用的比例，用户同意伪造和消除的评级。配备了这些定量措施，我们找到了一个封闭的形式解决方案的问题的最佳伪造和抑制率，并描述了隐私，伪造率和抑制率之间的权衡。一个流行的推荐系统上的实验结果表明，我们的方法可能有助于隐私增强。
英文摘要：
Recommendation systems are information-filtering systems that tailor information to users on the basis of knowledge about their preferences. The ability of these systems to profile users is what enables such intelligent functionality, but at the same time, it is the source of serious privacy concerns. In this paper we investigate a privacy-enhancing technology that aims at hindering an attacker in its efforts to accurately profile users based on the items they rate. Our approach capitalizes on the combination of two perturbative mechanisms---the forgery and the suppression of ratings. While this technique enhances user privacy to a certain extent, it inevitably comes at the cost of a loss in data utility, namely a degradation of the recommendation's accuracy. In short, it poses a trade-off between privacy and utility. The theoretical analysis of said trade-off is the object of this work. We measure privacy as the Kullback-Leibler divergence between the user's and the population's item distributions, and quantify utility as the proportion of ratings users consent to forge and eliminate. Equipped with these quantitative measures, we find a closed-form solution to the problem of optimal forgery and suppression of ratings, and characterize the trade-off among privacy, forgery rate and suppression rate. Experimental results on a popular recommendation system show how our approach may contribute to privacy enhancement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
