A Generic Federated Recommendation Framework via Fake Marks and Secret Sharing
Abstract
With the implementation of privacy protection laws such as GDPR, it is increasingly difficult for organizations to legally collect users’ data. However, a typical machine learning-based recommendation algorithm requires the data to learn users’ preferences. Some recent works thus turn to develop federated learning-based recommendation algorithms, but most of them either cannot protect the users’ privacy well, or sacrifice the model accuracy. In this article, we propose a lossless and generic federated recommendation framework via fake marks and secret sharing (FMSS). Our FMSS can not only protect the two types of users’ privacy, i.e., rating values and rating behaviors, without sacrificing the recommendation performance, but can also be applied to most recommendation algorithms for rating prediction, item ranking, and sequential recommendation. Specifically, we extend existing fake items to fake marks, and combine it with secret sharing to perturb the data uploaded by the clients to a server. We then apply our FMSS to six representative recommendation algorithms, i.e., MF-MPC and NeuMF for rating prediction, eALS and VAE-CF for item ranking, and Fossil and GRU4Rec for sequential recommendation. The experimental results demonstrate that our FMSS is a lossless and generic framework, which is able to federate a series of different recommendation algorithms in a lossless and privacy-aware manner.
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Secret Sharing (SS) methodology can be applied instead of HE to reduce communication overhead.
“Secret Sharing (SS) methodology can be applied instead of HE to reduce communication overhead [16, 133, 152, 153].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [153]
The SS approach is well-suited for FedRec, where minimizing communication overhead is another key concern alongside privacy.
“This approach is also well-suited for FedRec, where minimizing communication overhead is another key concern alongside privacy [16, 152, 153].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [153]
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- Authors
- Lin, Z., Pan, W., Yang, Q., & Ming, Z.
- Affiliations
- Shenzhen University (CN)
- Hong Kong University of Science and Technology (HK)
- Venue
- ACM Transactions on Information Systems
- Published
- 2022-12-21
- Volume
- 41
- Issue
- 2
- Pages
- 1-37
- ISSN
- 1046-8188 (ISSN-L)
- 1558-2868
- OpenAlex
- W4285015060
- Cited by
- 44
- References
- 55
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 17 · 2023: 6 · 2022: 1
- FWCI
- 3.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Information Systems (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61836005, 62172283)
- National Key Research and Development Program of China (2018AAA0101100)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- federated recommendation, secret sharing, user privacy protection, rating prediction, item ranking, sequential recommendation, federated learning, data perturbation, collaborative filtering, matrix factorization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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