Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda
Data Technologies and Applications
ISSN: 2514-9288
Article publication date: 4 May 2022
Issue publication date: 17 March 2023
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.
Keywords
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
Publisher
:Emerald Publishing Limited
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