Applying Differential Privacy to Matrix Factorization
Applying Differential Privacy to Matrix Factorization
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DOI：
10.1145/2792838.2800173
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发表时间：
2015-09
期刊：
影响因子：
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通讯作者：
Arnaud Berlioz;Arik Friedman;M. Kâafar;R. Boreli;S. Berkovsky
中科院分区：
文献类型：
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作者：
Arnaud Berlioz;Arik Friedman;M. Kâafar;R. Boreli;S. Berkovsky
推荐系统正日益成为在线服务的一个组成部分。由于这些建议依赖于个人用户信息，因此使用此类系统会导致固有的隐私损失。虽然有一些作品研究了隐私增强的基于邻居的推荐，但很少有人关注隐私保护的潜在因素模型，如矩阵分解技术。本文利用微分隐私这一严格且可证明的隐私保护方法，解决了矩阵分解的隐私保护问题。我们提出并研究了几种将差分隐私应用于矩阵分解的方法，并评估了每种方法提供的隐私准确性权衡。我们表明，输入扰动产生最佳的推荐精度，同时保证了坚实的隐私保护水平。
Recommender systems are increasingly becoming an integral part of on-line services. As the recommendations rely on personal user information, there is an inherent loss of privacy resulting from the use of such systems. While several works studied privacy-enhanced neighborhood-based recommendations, little attention has been paid to privacy preserving latent factor models, like those represented by matrix factorization techniques. In this paper, we address the problem of privacy preserving matrix factorization by utilizing differential privacy, a rigorous and provable privacy preserving method. We propose and study several approaches for applying differential privacy to matrix factorization, and evaluate the privacy-accuracy trade-offs offered by each approach. We show that input perturbation yields the best recommendation accuracy, while guaranteeing a solid level of privacy protection.