Privacy-preserving synthetic data generation for recommendation systems

Fan Liu, Zhiyong Cheng, Huilin Chen, Yinwei Wei, Liqiang Nie, Mohan Kankanhalli

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

15 Citations (Scopus)


Recommendation systems make predictions chiefly based on users' historical interaction data (e.g., items previously clicked or purchased). There is a risk of privacy leakage when collecting the users' behavior data for building the recommendation model. However, existing privacy-preserving solutions are designed for tackling the privacy issue only during the model training [32] and results collection [40] phases. The problem of privacy leakage still exists when directly sharing the private user interaction data with organizations or releasing them to the public. To address this problem, in this paper, we present a User Privacy Controllable Synthetic Data Generation model (short for UPC-SDG), which generates synthetic interaction data for users based on their privacy preferences. The generation model aims to provide certain privacy guarantees while maximizing the utility of the generated synthetic data at both data level and item level. Specifically, at the data level, we design a selection module that selects those items that contribute less to a user's preferences from the user's interaction data. At the item level, a synthetic data generation module is proposed to generate a synthetic item corresponding to the selected item based on the user's preferences. Furthermore, we also present a privacy-utility trade-off strategy to balance the privacy and utility of the synthetic data. Extensive experiments and ablation studies have been conducted on three publicly accessible datasets to justify our method, demonstrating its effectiveness in generating synthetic data under users' privacy preferences.

Original languageEnglish
Title of host publicationProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
EditorsLuke Gallagher, Qingyun Wu
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages11
ISBN (Electronic)9781450387323
Publication statusPublished - 2022
Externally publishedYes
EventACM International Conference on Research and Development in Information Retrieval 2022 - Madrid, Spain
Duration: 11 Jul 202215 Jul 2022
Conference number: 45th (Proceedings) (Website)


ConferenceACM International Conference on Research and Development in Information Retrieval 2022
Abbreviated titleSIGIR 2022
Internet address


  • privacy preference
  • privacy-preserving
  • recommendation
  • synthetic data generation

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