Abstract: The recommendation system needs to extract the historical data information of the relevant users on a large scale as the training set of the prediction model. The larger and more specific the amount of data provided by users is, the easier it is for the personal information to be inferred, which is easy to lead to the leakage of personal privacy, so that the user’s trust in the service provider is reduced and the relevant data are no longer provided for the system, resulting in the reduction of the system recommendation accuracy or even more difficult to complete the recommendation. Therefore, how to obtain user information for effective recommendation with high accuracy under the premise of protecting user privacy has become a research hotspot. This paper firstly summarizes the privacy-preserving technology, including differential privacy technology, homomorphic encryption technology, federated learning and secure multi-party computing technology, and compares these commonly used privacy-preserving tech-nologies. Then, from the perspective of balancing the relationship between privacy injection and recommendation accuracy, the privacy-preserving technologies adopted by the user side, the server side and the user-server side are introduced, and the research results of privacy-preserving in recommendation systems at home and abroad are systematically elaborated. Based on this, a summary, comparison, and analysis are conducted. Next, the experi-mental results of the recommendation algorithm based on differential privacy technology are compared, and the shortcomings of the corresponding technology are analyzed. Finally, the future development directions of the recommendation system based on privacy-preserving are prospected.

Key words: recommendation system, privacy-preserving, recommendation method, privacy technology

摘要： 推荐系统需要大规模地提取相关用户的历史数据信息作为预测模型的训练集，用户提供的数据量越大、越具体，个人信息越容易被推断出来，致使个人隐私的泄露，从而使用户对服务提供商的信任程度降低，不再向系统提供相关数据，导致系统推荐精度降低甚至较难完成推荐。因此，如何能在对用户隐私进行保护的前提下，获取用户信息进行准确性高的有效推荐已成为当前的一个研究热点。首先，对隐私保护技术进行概述，主要包括差分隐私技术、同态加密技术、联邦学习技术和安全多方计算技术，并对这几种常用的隐私保护技术进行了比较；然后，从平衡隐私注入和推荐精度之间的关系角度出发介绍客户端、服务器端和客户-服务器端所采用的隐私保护技术，系统阐述了国内外推荐系统隐私保护的研究成果，并在此基础上进行总结、对比和分析；接下来，对比基于差分隐私技术的推荐算法的实验效果，并分析相应技术存在的不足；最后，对基于隐私保护的推荐系统的未来发展方向提出相应的建议和展望。

关键词: 推荐系统, 隐私保护, 推荐方式, 隐私技术