Privacy protection is an essential issue in personalized news recommendation, and federated learning can potentially mitigate the privacy concern by training personalized news recommendation models over decentralized user data.For a theoretical privacy guarantee, differential privacy is necessary. However, applying differential privacy to federated recommendation training and serving conventionally suffers from the unsatisfactory trade-off between privacy and utility due to the high-dimensional characteristics of model gradients and hidden representations. In addition, there is no formal privacy guarantee for both training and serving in federated recommendation. In this paper, we propose a unified federated news recommendation method for effective and privacy-preserving model training and online serving with differential privacy guarantees. We first clarify the notion of differential privacy over users' behavior data for both model training and online serving in the federated recommendation scenario. Next, we propose a privacy-preserving online serving mechanism under this definition with differentially private user interest decomposition. More specifically, it decomposes the high-dimensional and privacy-sensitive user embedding into a combination of public basic vectors and adds noise to the combination coefficients. In this way, it can avoid the dimension curse and improve the utility by reducing the required noise intensity for differential privacy. Besides, we design a federated recommendation model training method with differential privacy, which can avoid the dimension-dependent noise for large models via label permutation and differentially private attention modules. Experiments on real-world news recommendation datasets validate the effectiveness of our method in achieving a good trade-off between privacy protection and utility for federated news recommendations.
翻译:保护隐私是个人化新闻建议中的一个基本问题,而联邦学习可以通过培训个人化新闻建议模式,对分散用户数据进行个人化新闻建议模式的培训,来减轻隐私关切。 为了提供理论隐私权保障,有必要实行不同的隐私。然而,在联邦建议培训和传统服务方面,对联邦建议培训适用不同的隐私,由于模型梯度和隐蔽表达方式的高度特点,隐私和实用性之间的权衡不能令人满意。此外,对于培训和提供联邦建议方面,没有正式的隐私保障。在本文件中,我们提出一个统一的联邦化新闻建议方法,用于有效和隐私保护模式培训,在网上提供不同的隐私保障。我们首先澄清在联邦建议情景中,对用户行为数据实行不同的隐私保密概念。我们提议根据这一定义建立隐私和实用性机制,但私人用户兴趣和隐蔽性代表的差别性特点不同。我们提议,将高度和对隐私敏感的用户嵌入一个公共基本矢量的组合,并增加组合系数的噪音。通过这种方式,我们可以避免使用对用户的保密性定义的隐私差异概念概念概念,并且改进用户行为数据的实用性,同时通过联邦标准标准标准,通过降低对保密性标准要求的保密性标准,从而降低对标准要求的精确化标准要求的深度化标准要求的深度化标准,从而降低对标准要求的精确化标准化标准化的深度化标准,从而降低对标准要求对标准要求使用。