With the development of the internet, recommending interesting products to users has become a highly valuable research topic for businesses. Recommendation systems play a crucial role in addressing this issue. To prevent the leakage of each user's (client's) private data, Federated Recommendation Systems (FedRec) have been proposed and widely used. However, extensive research has shown that FedRec suffers from security issues such as data privacy leakage, and it is challenging to train effective models with FedRec when each client only holds interaction information for a single user. To address these two problems, this paper proposes a new privacy-preserving recommendation system (PRSI), which includes a preprocessing module and two main phases. The preprocessing module employs split vectors and fake interaction items to protect clients' interaction information and recommendation results. The two main phases are: (1) the collection of interaction information and (2) the sending of recommendation results. In the interaction information collection phase, each client uses the preprocessing module and random communication methods (according to the designed interactive protocol) to protect their ID information and IP addresses. In the recommendation results sending phase, the central server uses the preprocessing module and triplets to distribute recommendation results to each client under secure conditions, following the designed interactive protocol. Finally, we conducted multiple sets of experiments to verify the security, accuracy, and communication cost of the proposed method.

翻译：随着互联网的发展，向用户推荐感兴趣的产品已成为企业极具价值的研究课题。推荐系统在解决该问题中发挥着关键作用。为防止每个用户（客户端）的隐私数据泄露，联邦推荐系统（FedRec）被提出并得到广泛应用。然而，大量研究表明FedRec存在数据隐私泄露等安全问题，且当每个客户端仅持有单个用户的交互信息时，难以通过FedRec训练出有效的模型。为解决这两个问题，本文提出一种新的隐私保护推荐系统（PRSI），该系统包含预处理模块和两个主要阶段。预处理模块采用分割向量和伪造交互项来保护客户端的交互信息与推荐结果。两个主要阶段分别为：（1）交互信息收集阶段；（2）推荐结果发送阶段。在交互信息收集阶段，各客户端利用预处理模块和随机通信方式（依据设计的交互协议）保护其ID信息与IP地址。在推荐结果发送阶段，中央服务器依据设计的交互协议，利用预处理模块和三重组在安全条件下向各客户端分发推荐结果。最后，我们通过多组实验验证了所提方法的安全性、准确性和通信成本。