Recommender systems can be privacy-sensitive. To protect users' private historical interactions, federated learning has been proposed in distributed learning for user representations. Using federated recommender (FedRec) systems, users can train a shared recommendation model on local devices and prevent raw data transmissions and collections. However, the recommendation model learned by a common FedRec may still be vulnerable to private information leakage risks, particularly attribute inference attacks, which means that the attacker can easily infer users' personal attributes from the learned model. Additionally, traditional FedRecs seldom consider the diverse privacy preference of users, leading to difficulties in balancing the recommendation utility and privacy preservation. Consequently, FedRecs may suffer from unnecessary recommendation performance loss due to over-protection and private information leakage simultaneously. In this work, we propose a novel user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users by paying a minimum recommendation accuracy price. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent. Experiments conducted on different real-world datasets demonstrate that our framework is more efficient and flexible compared to baselines.

翻译：推荐系统可能涉及隐私敏感问题。为了保护用户私密的历史交互数据，联邦学习被提出用于分布式学习用户表示。通过使用联邦推荐系统，用户可以在本地设备上训练共享推荐模型，并防止原始数据传输和收集。然而，普通联邦推荐系统学习的推荐模型仍可能面临隐私信息泄露风险，特别是属性推断攻击——攻击者可轻易从学习模型中推断用户个人属性。此外，传统联邦推荐系统很少考虑用户多样化的隐私偏好，导致推荐效用与隐私保护难以平衡。因此，联邦推荐系统可能因过度保护而遭受不必要的推荐性能损失，同时存在隐私泄露风险。本文提出一种新型用户同意的联邦推荐系统（UC-FedRec），通过支付最小的推荐准确率成本来灵活满足用户不同的隐私需求。UC-FedRec允许用户自定义隐私偏好以满足多样化需求，并在用户同意下进行推荐。在多个真实数据集上进行的实验表明，与基线方法相比，我们的框架更加高效且灵活。