Point-of-Interest (POI) recommendation has been extensively studied and successfully applied in industry recently. However, most existing approaches build centralized models on the basis of collecting users’ data. Both private data and models are held by the recommender, which causes serious privacy concerns. In this article, we propose a novel Privacy preserving POI Recommendation (PriRec) framework. First, to protect data privacy, users’ private data (features and actions) are kept on their own side, e.g., Cellphone or Pad. Meanwhile, the public data that need to be accessed by all the users are kept by the recommender to reduce the storage costs of users’ devices. Those public data include: (1) static data only related to the status of POI, such as POI categories, and (2) dynamic data dependent on user-POI actions such as visited counts. The dynamic data could be sensitive, and we develop local differential privacy techniques to release such data to the public with privacy guarantees. Second, PriRec follows the representations of Factorization Machine (FM) that consists of a linear model and the feature interaction model. To protect the model privacy, the linear models are saved on the users’ side, and we propose a secure decentralized gradient descent protocol for users to learn it collaboratively. The feature interaction model is kept by the recommender since there is no privacy risk, and we adopt a secure aggregation strategy in a federated learning paradigm to learn it. To this end, PriRec keeps users’ private raw data and models in users’ own hands, and protects user privacy to a large extent. We apply PriRec in real-world datasets, and comprehensive experiments demonstrate that, compared with FM, PriRec achieves comparable or even better recommendation accuracy.

中文翻译：

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实用的隐私保护 POI 建议

最近，兴趣点（POI）推荐在工业中得到了广泛的研究和成功应用。然而，大多数现有方法都是在收集用户数据的基础上构建集中式模型。私人数据和模型都由推荐者持有，这会引起严重的隐私问题。在本文中，我们提出了一种新颖的隐私保护 POI 推荐 (PriRec) 框架。首先，为了保护数据隐私，用户的私人数据（特征和行为）被保存在他们自己的一方，例如手机或平板电脑。同时，推荐器保留所有用户需要访问的公共数据，以降低用户设备的存储成本。这些公共数据包括：（1）仅与 POI 状态相关的静态数据，例如 POI 类别，以及（2）依赖于用户 POI 操作的动态数据，例如访问次数。动态数据可能是敏感的，我们开发了本地差分隐私技术，以在隐私保证的情况下向公众发布这些数据。其次，PriRec 遵循由线性模型和特征交互模型组成的因子分解机 (FM) 的表示。为了保护模型隐私，将线性模型保存在用户端，我们提出了一种安全的分散梯度下降协议供用户协同学习。特征交互模型由推荐者保留，因为没有隐私风险，我们采用联邦学习范式中的安全聚合策略来学习它。为此，PriRec 将用户的私有原始数据和模型保存在用户自己手中，在很大程度上保护了用户隐私。我们在现实世界的数据集中应用 PriRec，