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Probabilistic matrix factorization with personalized differential privacy

机译：具有个性化差异隐私的概率矩阵分解

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摘要

Probabilistic matrix factorization (PMF) plays a crucial role in recommendation systems. It requires a large amount of user data (such as user shopping records and movie ratings) to predict personal preferences, and thereby provides users high-quality recommendation services, which expose the risk of leakage of user privacy. Differential privacy, as a provable privacy protection framework, has been applied widely to recommendation systems. It is common that different individuals have different levels of privacy requirements on items. However, traditional differential privacy can only provide a uniform level of privacy protection for all users.In this paper, we mainly propose a probabilistic matrix factorization recommendation scheme with personalized differential privacy (PDP-PMF). It aims to meet users' privacy requirements specified at the item-level instead of giving the same level of privacy guarantees for all. We then develop a modified sampling mechanism (with bounded differential privacy) for achieving PDP. We also perform a theoretical analysis of the PDP-PMF scheme and demonstrate the privacy of the PDP-PMF scheme. In addition, we implement our proposed probabilistic matrix factorization schemes both with traditional and with personalized differential privacy (DP-PMF, PDP-PMF). A series of experiments are performed on real datasets to demonstrate the superior performance of PDP-PMF in recommendation quality. (C) 2019 Elsevier B.V. All rights reserved.
机译：概率矩阵分解（PMF）在推荐系统中起着至关重要的作用。它需要大量的用户数据（例如用户购物记录和电影收视率）来预测个人喜好，从而为用户提供高质量的推荐服务，从而暴露出泄露用户隐私的风险。作为可证明的隐私保护框架，差异隐私已广泛应用于推荐系统。通常，不同的个人对商品的隐私要求级别不同。然而，传统的差异隐私只能为所有用户提供统一级别的隐私保护。本文主要提出了一种带有个性化差异隐私的概率矩阵分解推荐方案（PDP-PMF）。它旨在满足在项目级别指定的用户隐私要求，而不是为所有人提供相同级别的隐私保证。然后，我们开发一种改进的采样机制（具有有限的差分隐私）以实现PDP。我们还对PDP-PMF方案进行了理论分析，并演示了PDP-PMF方案的私密性。另外，我们用传统的和个性化的差分隐私（DP-PMF，PDP-PMF）实现了我们提出的概率矩阵分解方案。在真实数据集上进行了一系列实验，以证明PDP-PMF在推荐质量上的优越性能。 （C）2019 Elsevier B.V.保留所有权利。

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