Matrix factorization recommender based on adaptive Gaussian differential privacy for implicit feedback
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To achieve privacy protection, centralized RS typically uses non-cryptographic techniques due to their ease of application and low computational complexity.
“To achieve privacy protection, centralized RS typically uses non-cryptographic techniques due to their ease of application and low computational complexity [8, 123-125].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [123]
In a centralized setup, the system is a trusted server responsible for maintaining data privacy.
“In a centralized setup, the system is a trusted server responsible for maintaining data privacy [99, 123-126].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [123]
Laplace, Gaussian, and Exponential mechanisms are commonly applied for noise addition to satisfy DP.
“Usually, Laplace [14, 15, 171, 172, 176, 177], Gaussian [101, 123, 125] & Exponential [126] mechanisms are commonly applied for noise addition to satisfy the DP.”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [123]
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- Authors
- Liu, H., Wang, Y., Zhang, Z., Deng, J., Chen, C., & Zhang, L. Y.
- Affiliations
- Chongqing University of Posts and Telecommunications (CN)
- RMIT University (AU)
- Griffith University (AU)
- Corresponding author
- Wang, Yong
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 61
- Issue
- 4
- Pages
- 103720
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W4393038469
- Cited by
- 34
- References
- 44
- Citations by year
- 2026: 13 · 2025: 17 · 2024: 4
- FWCI
- 5.04
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- Information Processing & Management (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Griffith Research Online (submittedVersion, cc-by-nc-nd)
- 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
- matrix factorization, differential privacy, Gaussian differential privacy, implicit feedback, privacy-preserving recommendation, adaptive clipping, Bayesian Personalized Ranking, gradient perturbation, recommender systems
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