Federated Matrix Factorization Recommendation Based on Secret Sharing for Privacy Preserving
Abstract
Traditional recommendation systems require users to upload local data to the server to generate recommendation results. In this process, users’ privacy is easy to disclose. Federated recommendations can solve the problem of local data privacy leakage, but the intermediate computing results are not protected. The existing work mainly protects the information in this process through encryption or disturbance schemes, which will result in complex calculations or low accuracy. Also, the two schemes only protect the rating data and process parameter information, but the existence information is not protected. Aiming at the above problems, this article proposes a federated matrix factorization based on secret sharing (FMFSS) to protect users’ privacy. The parameters are randomly divided into pieces, and then, the secret sharing technology is used to transmit private information between user–user and user–server, which does not introduce additional encryption cost and ensures value privacy, process privacy, and existence privacy. In addition, this article introduces the user–item interaction value, which is transmitted to the server with gradient information. In this way, the real gradient of the user cannot be inferred from the information received by the server from all parties, but the aggregated final average parameter information is real. The experimental comparison with the existing work and the analysis of computation time show that the proposed method can ensure the accuracy…
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Collaborative filtering-based approaches are more widely used than other recommender system categories.
“Among these, CF-based approaches are more widely used [9, 13-16].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [16]
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 [16]
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 [16]
FedRec ensures protection of users' rated items' value privacy by allowing them to function the model locally, but it cannot assure existence privacy at the time of sharing gradients.
“The FedRec ensure the protection of users' rated items' value privacy by allowing them to function the model locally, but it could not assure the existence privacy at the time of sharing gradients [16].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [16]
Homomorphic Encryption (HE) is the most widely used cryptography-based approach in recommender systems, offering diverse computational capabilities of the ciphertext without decryption.
“Homomorphic Encryption (HE) , the most widely used cryptography-based approach in RS, offers diverse computational capabilities of the ciphertext without decryption [16].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [16]
Cites
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772 · see citing works
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988575 · Cited by 2,400 · see citing works
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377 · see citing works
A Generic Federated Recommendation Framework via Fake Marks and Secret Sharing
Lin, Z., Pan, W., Yang, Q., & Ming, Z. (2023) · ACM Transactions on Information Systems · doi:10.1145/3548456 · Cited by 44 · see citing works
Efficient Federated Matrix Factorization Against Inference Attacks
Chai, D., Wang, L., Chen, K., & Yang, Q. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501812 · Cited by 20 · see citing works
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Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651
- Authors
- Zheng, X., Guan, M., Jia, X., Sun, L., & Luo, Y.
- Affiliations
- Anhui Normal University (CN)
- Venue
- IEEE Transactions on Computational Social Systems
- Published
- 2024-06
- Volume
- 11
- Issue
- 3
- Pages
- 3525-3535
- ISSN
- 2329-924X (ISSN-L)
- 2373-7476
- OpenAlex
- W4387682271
- Cited by
- 14
- References
- 5
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 2
- FWCI
- 1.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Computational Social Systems (publishedVersion)
- Funders
- Natural Science Foundation of China (62272006)
- Major Natural Science Research Project for Anhui Universities (2023AH040026)
- Key Research and Development Project for Wuhu City (2022yf55)
- Natural Science Foundation of Anhui Province (2108085MF214)
- Key Program in the Youth Elite Support Plan in Universities of Anhui Province (gxyqZD2019010)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- secret sharing, privacy-preserving recommendation, user-item interactions, gradient privacy
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