Review of Privacy-Preserving Research in Recommendation Systems

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.

Keywords

• recommendation system; privacy-preserving; recommendation method; privacy technology