HN3S: A Federated AutoEncoder framework for Collaborative Filtering via Hybrid Negative Sampling and Secret Sharing
Integrity signals
Registry
Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [152]
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 [152]
Cites
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
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Cited by
Sorted by citations
Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651
Self-citation
- Authors
- Zhang, L., Li, G., Yuan, L., Ding, X., & Rong, Q.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Yuan, Ling
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2024-03
- Volume
- 61
- Issue
- 2
- Pages
- 103580
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W4388992110
- Cited by
- 9
- References
- 2
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Processing & Management (publishedVersion)
- Funders
- National Natural Science Foundation of China (62272176, 62272180)
- 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 recommender systems, collaborative filtering, privacy preservation, secret sharing, communication overhead, user privacy
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.
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