Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning
Open accessAbstract
Recommendation is one of the critical applications that helps users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. Prior work obfuscates user-item data before sharing it with recommendation system. This approach does not explicitly address the quality of recommendation while performing data obfuscation. Moreover, it cannot protect users against private-attribute inference attacks based on recommendations. This work is the first attempt to build a Recommendation with Attribute Protection (RAP) model which simultaneously recommends relevant items and counters private-attribute inference attacks. The key idea of our approach is to formulate this problem as an adversarial learning problem with two main components: the private attribute inference attacker, and the Bayesian personalized recommender. The attacker seeks to infer users' private-attribute information according to their items list and recommendations. The recommender aims to extract users' interests while employing the attacker to regularize the recommendation process. Experiments show that the proposed model both preserves the quality of recommendation service and protects users against private-attribute inference attacks.
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Users' private attributes typically include age, gender, political opinions, income, etc., which individuals prefer to keep confidential.
“Users' private attributes typically include age, gender, political opinions, income, etc., which individuals prefer to keep confidential [10, 99].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [99]
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 [99]
Considering the risk of adversarial attacks, adversarial learning can be utilized to detect privacy vulnerabilities within the system, helping to mitigate privacy-related threats.
“Considering the risk of adversarial attacks, adversarial learning can be utilized to detect privacy vulnerabilities within the system, helping to mitigate privacy-related threats [99, 116].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [99]
In PPRS, the AL technique can be implemented with two components: the recommendation module and the inference attack module.
“In PPRS, the AL technique can be implemented with two components: the recommendation module and the inference attack module [99].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [99]
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Hu, H., Cao, Y., He, Z., Tan, S., & Kan, M. (2023) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3624918.3625318 · Cited by 4 · see citing works
A Feature-Driven Approach for Identifying Pathogenic Social Media Accounts
Alvari, H., Beigi, G., Sarkar, S., Ruston, S. W. V., Corman, S. R., Davulcu, H., & Shakarian, P. (2020) · International Conference on Data Intelligence and Security (ICDIS) · doi:10.1109/icdis50059.2020.00010 · Cited by 4 · see citing works
Self-citationModular Debiasing of Latent User Representations in Prototype-Based Recommender Systems
Melchiorre, A. B., Masoudian, S., Kumar, D., & Schedl, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70341-6_4 · Cited by 3 · see citing works
PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation
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Social Relation-Level Privacy Risks and Preservation in Social Recommender Systems
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FedHoG: Federated Homogeneous Graph Neural Network for Privacy-Preserving Recommendation
Xian, Z., Yang, E., Pan, W., & Ming, Z. (2026) · ACM Transactions on Information Systems · doi:10.1145/3787468 · Cited by 2 · see citing works
SBlur: An Obfuscation Approach for Preserving Sensitive Attributes in Recommender System
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LEGO: A Lightweight and Efficient Multiple-Attribute Unlearning Framework for Recommender Systems
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Multi-Step Adversarial Perturbations on Recommender Systems EmbeddingsPreprint
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"Let's Eat Grandma": When Punctuation Matters in Sentence Representation for Sentiment Analysis.Preprint
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Self-citationXiong, Y., Guo, Y., Pan, W., Yang, Q., Ming, Z., Zhang, X., Han, Y., Lin, T., & Tang, X. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3565793 · Cited by 1 · see citing works
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Forget Me If You Can: Auditing User Data Revocation in Recommendation Systems
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Research on machine learning technology with privacy protection strategy in recommendation field
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Self-citationToward Privacy and Utility Preserving Image RepresentationPreprint
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- Authors
- Beigi, G., Mosallanezhad, A., Guo, R., Alvari, H., Nou, A., & Liu, H.
- Affiliations
- Arizona State University (US)
- Arizona State University, Tempe, AZ, USA
- Venue
- Proceedings of the 13th International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2020-01-20
- Pages
- 34-42
- OpenAlex
- W2989872778
- Cited by
- 80
- References
- 50
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 17 · 2023: 12 · 2022: 11 · 2021: 13 · 2020: 8
- FWCI
- 4.20
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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
- adversarial learning, recommendation quality, user privacy protection
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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