Global and Local Differential Privacy for Collaborative Bandits
Abstract
Collaborative bandit learning has become an emerging focus for personalized recommendation. It leverages user dependence for joint model estimation and recommendation. As such online learning solutions directly learn from users, e.g., result clicks, they bring in new challenges in privacy protection. Despite the existence of recent studies about privacy in contextual bandit algorithms, how to efficiently protect user privacy in a collaborative bandit learning environment remains unknown.
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The collaborative filtering-based recommender system approach can be problematic in terms of fairness and privacy.
“However, this approach can be problematic in terms of fairness and privacy [13-15, 18, 19].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [15]
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 [15]
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 [15]
If the recommender system is not trusted, DP can be applied at the user's end, known as Local Differential Privacy (LDP).
“However, if the RS is not trusted, DP can be applied at the user's end, known as Local Differential Privacy (LDP) [15].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [15]
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- Authors
- Wang, H., Zhao, Q., Wu, Q., Chopra, S., Khaitan, A., & Wang, H.
- Affiliations
- University of Virginia (US)
- Bloomberg (United States) (US)
- Venue
- Fourteenth ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2020-09-22
- Pages
- 150-159
- OpenAlex
- W3088648627
- Cited by
- 28
- References
- 31
- Citations by year
- 2025: 3 · 2024: 6 · 2023: 8 · 2022: 7 · 2021: 4
- FWCI
- 1.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Recommender Systems (RecSys) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- differential privacy, local differential privacy, user privacy, personalized recommendation, contextual bandits, online learning, privacy-preserving recommendation
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