Towards user-oriented privacy for recommender system data: A personalization-based approach to gender obfuscation for user profiles
Open accessAbstract
In this paper, we propose a new privacy solution for the data used to train a recommender system, i.e., the user–item matrix. The user–item matrix contains implicit information, which can be inferred using a classifier, leading to potential privacy violations. Our solution, called Personalized Blurring (PerBlur), is a simple, yet effective, approach to adding and removing items from users’ profiles in order to generate an obfuscated user–item matrix. The novelty of PerBlur is personalization of the choice of items used for obfuscation to the individual user profiles. PerBlur is formulated within a user-oriented paradigm of recommender system data privacy that aims at making privacy solutions understandable, unobtrusive, and useful for the user. When obfuscated data is used for training, a recommender system algorithm is able to reach performance comparable to what is attained when it is trained on the original, unobfuscated data. At the same time, a classifier can no longer reliably use the obfuscated data to predict the gender of users, indicating that implicit gender information has been removed. In addition to introducing PerBlur, we make several key contributions. First, we propose an evaluation protocol that creates a fair environment to compare between different obfuscation conditions. Second, we carry out experiments that show that gender obfuscation impacts the fairness and diversity of recommender system results. In sum, our work establishes that a simple, transparent approach to gender obfuscation can protect user privacy while at the same time improving recommendation results for users by maintaining fairness and enhancing diversity.
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Biased recommender systems are vulnerable to privacy breaches, where adversaries can exploit biased recommendations to access users' private information.
“Moreover, biased systems are vulnerable to privacy breaches, where adversaries can exploit biased recommendations to access users' private information, posing a threat to the RS [10, 11].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [10]
Section 1.2 (page unavailable) describes an attacker using recommender-system data to infer users’ gender, supporting the privacy-risk portion of the claim. But the paper’s threat model concerns access to the user–item matrix, not exploiting biased recommendations; section 2.4 discusses bias as a separate fairness issue. (LLM, conf=0.86)
Experimental — accuracy not yet measuredA black box attack remains challenging due to the need for knowledge about the RS algorithm or architecture, though possible.
“However, it remains challenging due to the need for knowledge about the RS algorithm or architecture, though possible [10, 8991, 93, 95-97].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [10]
Section 1.2 (page unavailable) describes a possible black-box gender-inference attack in which the attacker has a trained classifier and the user–item matrix. It defines “blackbox” as lacking information about obfuscation; it does not say the attack is challenging because it requires knowledge of the recommender-system algorithm or architecture. (LLM, conf=0.78)
Experimental — accuracy not yet measuredBased on user-item interaction data, the adversary exploits the relationship between user preferences, behaviours, recommendations, and data biases to infer hidden attributes like demographic information, interests, or other personal details.
“Based on user-item interaction data, the adversary exploits the relationship between user preferences, behaviours, recommendations, and data biases to infer hidden attributes like demographic information, interests, or other personal details [10].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [10]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredUsers' 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 [10]
Section 2.3, “Gender inference” (page not provided), identifies age, gender, and political views as sensitive attributes that a user may consider private. The cited passages do not identify income as one of those attributes, so the full list is not supported by this source. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSlokom et al. [10] explained that gender bias in data harms fairness in recommendations and creates opportunities for attribute inference attacks, threatening users' privacy.
“Slokom et al. [10] explained that gender bias in data harms fairness in recommendations and creates opportunities for attribute inference attacks, threatening users' privacy.”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [10]
Section 7 (Maintaining fairness) reports gender gaps in recommendation performance, while sections 1.2 (Threat model for gender inference) and 2.3 (Gender inference) describe privacy-threatening inference of attributes from user data. The paper does not establish the claim's causal link that gender bias in the data creates those inference opportunities or causes the fairness gaps. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Implicit data can pose a risk, as adversaries may exploit it to deduce sensitive user information.
“However, this implicit data can pose a risk, as adversaries may exploit it to deduce sensitive user information.”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [10]
A server can undergo a 'mission creep,' starting honestly but later changing its business strategy to infer or collect users' private data, or it may be transferred to another company that attempts to do so.
“Additionally, a server can undergo a “mission creep,” starting honestly but later changing its business strategy to infer or collect users' private data. Alternatively, it may be transferred to another company that attempts to do so [10].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [10]
Obfuscation can be achieved by solving a multi-objective optimization problem where the objectives are improving RS accuracy and protecting users' privacy, or applying probabilistic privacy mapping between private and to-be-released public data, or removing the most indicative items of the sensitive data with the addition of some fictitious data to confuse the adversary.
“The obfuscation can be achieved by solving the multi-objective optimization problem where the objectives are improving RS accuracy & protecting users' privacy [119] or applying probabilistic privacy mapping between private & to be released public data [100] or removing the most indicative items of the sensitive data with the addition of some fictitious data to confuse the adversary [10, 11”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [10]
Slokom et al. [10] used obfuscation techniques to address this privacy issue by mixing users' rated data in a way that an adversary can not infer users' gender from their ratings.
“They used obfuscation techniques to address this privacy issue by mixing users' rated data in a way that an adversary can not infer users' gender from their ratings.”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [10]
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Feng, T., Guo, Y., & Chen, Y. (2015) · Computer Communications · doi:10.1016/j.comcom.2015.06.013 · Cited by 9 · see citing works
Collaborative Autoencoder for Recommender SystemsPreprint
Li, Q., Zheng, X., & Wu, X. (2017) · arXiv (Cornell University) · Cited by 4 · see citing works
BlUrM(or)e : Revisiting gender obfuscation in the user-item matrix
Strucks, C., Slokom, M., & Larson, M. (2019) · Research Repository (Delft University of Technology) · Cited by 4 · see citing works
Self-citationPrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation
Hu, G., & Yang, Q. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.404 · Cited by 3 · see citing works
Can user gender and recommendation performance be preserved simultaneously?
Feng, T., Guo, Y., & Chen, Y. (2015) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2015.7069345 · Cited by 2 · see citing works
Data Masking for Recommender Systems: Prediction Performance and Rating Hiding
Slokom, M., Larson, M., Hanjalić, A., Tkalcic, M., & Pera, M. S. (2019) · Radboud Repository (Radboud University) · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
2 of the 97 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Fairness in recommender systems: research landscape and future directions
Deldjoo, Y., Jannach, D., Bellogín, A., Difonzo, A., & Zanzonelli, D. (2023) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-023-09364-z · Cited by 258 · see citing works
A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering
Rostami, M., Oussalah, M., & Farrahi, V. (2022) · IEEE Access · doi:10.1109/access.2022.3175317 · Cited by 161 · see citing works
Developing a Personalized E-Learning and MOOC Recommender System in IoT-Enabled Smart Education
Amin, S., Uddin, M. I., Mashwani, W. K., Alarood, A. A., Alzahrani, A., & Alzahrani, A. O. (2023) · IEEE Access · doi:10.1109/access.2023.3336676 · Cited by 69 · see citing works
Pramod, D. (2022) · Data Technologies and Applications · doi:10.1108/dta-02-2022-0083 · Cited by 37 · see citing works
Detecting shilling groups in online recommender systems based on graph convolutional network
Wang, S., Zhang, P., Wang, H., Yu, H., & Zhang, F. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103031 · Cited by 37 · see citing works
Liu, H., Wang, Y., Zhang, Z., Deng, J., Chen, C., & Zhang, L. Y. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103720 · Cited by 34 · see citing works
A systematic review of privacy techniques in recommendation systems
Ogunseyi, T. B., Avoussoukpo, C. B., & Jiang, Y. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00710-1 · Cited by 27 · see citing works
Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation
Vassøy, B., & Helge Langseth, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10663-5 · Cited by 22 · see citing works
Exploring the Efforts of IAN-BGRU Justifications in Food Recommender System and its User Preferences
Haris, A. A., Vinothkumar, E. S., Nithya, N. S., Sharma, N., Allirani, P., & Gayathri, B. (2023) · International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) · doi:10.1109/icssas57918.2023.10331717 · Cited by 20 · see citing works
Food recommendation towards personalized wellbeing
Qiao, G., Zhang, D., Zhang, N., Shen, X., Jiao, X., Lu, W., Fan, D., Zhao, J., Zhang, H., Chen, W., et al. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.104877 · Cited by 19 · see citing works
Abumalloh, R. A., Nilashi, M., Halabi, O., & Ali, R. (2024) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2024.100569 · Cited by 17 · see citing works
Escobedo, G., Moscati, M., Müllner, P., Kopeinik, S., Kowald, D., Lex, E., & Schedl, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70368-3_21 · Cited by 12 · see citing works
Event-set differential privacy for fine-grained data privacy protection
Wang, T., Yang, W., Ma, X., & Wang, B. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.10.006 · Cited by 6 · see citing works
Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit
Vente, T., Ekstrand, M. D., & Beel, J. (2023) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3604915.3610656 · Cited by 6 · see citing works
How to Diversify any Personalized Recommender?
Slokom, M., Daniil, S., & Laura Hollink, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88717-8_23 · Cited by 4 · see citing works
Self-citationPrivacy-Preserving Recommendation with Debiased Obfuscaiton
Lin, C., Liu, B., Zhang, X., Wang, Z., Hu, C., & Luo, L. (2022) · 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom56396.2022.00086 · Cited by 4 · see citing works
Martínez, A., Tufiş, M., & Boratto, L. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657858 · Cited by 3 · see citing works
Boratto, L., Faralli, S., Marras, M., & Stilo, G. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102791 · Cited by 3 · see citing works
SBlur: An Obfuscation Approach for Preserving Sensitive Attributes in Recommender System
Roy, F., Zhao, N., & Ding, X. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104282 · Cited by 2 · see citing works
Privacy Preserving Recommender Systems
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Advancing SDG5: Machine Learning and Statistical Graphics for Women’s Empowerment and Gender Equity
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Slokom, M., & Larson, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.03275 · Cited by 1 · see citing works
Self-citationFood Recognition System and Nutrition Tracking Using Convolutional Neural Network Model
Indhumathi, P., Jayakumar, T., Ragul, K., & Saravana Bharathi, S. (2024) · Asia Pacific Conference on Innovation in Technology (APCIT) · doi:10.1109/apcit62007.2024.10673543
Opt-in Transparent Fairness for Recommender Systems
Vassøy, B., Kille, B., & Helge Langseth, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88708-6_23
Resisting Bag-Based Attribute Profiling by Adding Adversarial Items to Existing Media Profiles
Liu, Z., Zhao, Z., & Larson, M. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3574977
Self-citationYang, M., Ni, D., Lu, X., & Head, M. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5339299
A Fair Enhanced Bayesian Personalized Ranking Using Adversarial Learning
Ngaffo, A. N., Albert, J., Frénay, B., & Perrouin, G. (2025) · ACM Transactions on Recommender Systems · doi:10.1145/3749106
Yang, M., Fu, F., Wei, Y., Lu, X., & Ni, D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6668965
Differentially Private and Fair Algorithmic Hiring for Recruiters
Davidopoulos, S., Sacharidis, D., & Symeonidis, P. (2026) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3774935.3806147
Recommend Locally, Diversify Globally: FedFlex for Live Federated Netflix Recommendations
Lankester, S., Bertoli, G. D. C., Vizcaino, M., Beauxis-Aussalet, E., & Slokom, M. (2026) · International Conference on Federated Learning and Intelligent Computing Systems (FLICS) · doi:10.1109/flics70075.2026.11621914
Self-citationRoy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651
“…deals with large datasets containing millions of • The storage requirements for encrypted data and the interme − • Require frequent data exchange between client and system or • The added computational and communication overhead can • Some methodology, such as secret sharing, requires constant”
- Authors
- Slokom, M., Hanjalic, A., & Larson, M.
- Affiliations
- Delft University of Technology (NL)
- Radboud University Nijmegen (NL)
- Corresponding author
- Slokom, Manel
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 58
- Issue
- 6
- Pages
- 102722
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W3201577120
- Cited by
- 31
- References
- 97
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 7 · 2023: 6 · 2022: 5 · 2021: 2
- FWCI
- 1.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Information Processing & Management (publishedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- 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
- user-item matrix, user profiles, recommendation fairness, recommendation diversity
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.
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