Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda

Abstract

Purpose

This study explores privacy challenges in recommender systems (RSs) and how they have leveraged privacy-preserving technology for risk mitigation. The study also elucidates the extent of adopting privacy-preserving RSs and postulates the future direction of research in RS security.

Design/methodology/approach

The study gathered articles from well-known databases such as SCOPUS, Web of Science and Google scholar. A systematic literature review using PRISMA was carried out on the 41 papers that are shortlisted for study. Two research questions were framed to carry out the review.

Findings

It is evident from this study that privacy issues in the RS have been addressed with various techniques. However, many more challenges are expected while leveraging technology advancements for fine-tuning recommenders, and a research agenda has been devised by postulating future directions.

Originality/value

The study unveils a new comprehensive perspective regarding privacy preservation in recommenders. There is no promising study found that gathers techniques used for privacy protection. The study summarizes the research agenda, and it will be a good reference article for those who develop privacy-preserving RSs.

Citation

Pramod, D. (2023), "Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda", Data Technologies and Applications, Vol. 57 No. 1, pp. 32-55. https://doi.org/10.1108/DTA-02-2022-0083