Latest trends of security and privacy in recommender systems: A comprehensive review and future perspectives
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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?
Conformity bias is frequently observed in users' ratings and reviews.
“This bias is frequently observed in users' ratings [62, 63] and reviews [26].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [26]
Cites
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works
<i>L</i> -diversity: Privacy beyond <i>k</i> -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617 · see citing works
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451 · see citing works
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2918951 · Cited by 2,455 · see citing works
Filter Bubbles, Echo Chambers, and Online News Consumption
Flaxman, S., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw006 · Cited by 1,992 · see citing works
The use of machine learning algorithms in recommender systems: a systematic review
Portugal, I., Alencar, P., & Cowan, D. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.12.020 · Cited by 743 · see citing works
Baeza-Yates, R. (2018) · Communications of the ACM · doi:10.1145/3209581 · Cited by 432 · see citing works
HIPAA regulations - a new era of medical-record privacy?
Annas, G. J. (2003) · New England Journal of Medicine · doi:10.1056/nejmlim035027 · Cited by 402 · see citing works
"You Might Also Like:" Privacy Risks of Collaborative Filtering
Calandrino, J. A., Kilzer, A., Narayanan, A., Felten, E. W., & Shmatikov, V. (2011) · 2011 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2011.40 · Cited by 311 · see citing works
Bruns, A. (2019) · Internet Policy Review · doi:10.14763/2019.4.1426 · Cited by 193 · see citing works
Online Price Discrimination and EU Data Privacy Law
Zuiderveen Borgesius, F., & Poort, J. (2017) · Journal of Consumer Policy · doi:10.1007/s10603-017-9354-z · Cited by 181 · see citing works
Beyond Parity: Fairness Objectives for Collaborative Filtering
Yao, S., & Huang, B. (2017) · arXiv · doi:10.48550/arxiv.1705.08804 · Cited by 169 · see citing works
Zhang, S., Li, X., Tan, Z., Peng, T., & Wang, G. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.053 · Cited by 163
Black Box Attacks on Explainable Artificial Intelligence(XAI) methods in Cyber Security
Kuppa, A., & Le-Khac, N.-A. (2020) · 2020 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206780 · Cited by 123 · see citing works
Adversarial attacks on an oblivious recommender
Christakopoulou, K., & Banerjee, A. (2019) · Proceedings of the 13th ACM Conference on Recommender Systems · doi:10.1145/3298689.3347031 · Cited by 105 · see citing works
Anti-discrimination learning: a causal modeling-based framework
Zhang, L., & Wu, X. (2017) · International Journal of Data Science and Analytics · doi:10.1007/s41060-017-0058-x · Cited by 48 · see citing works
Flagged first, then by citations
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Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651
- Authors
- Himeur, Y., Sohail, S. S., Bensaali, F., Amira, A., & Alazab, M.
- Affiliations
- Qatar University (QA)
- Jamia Hamdard (IN)
- University of Sharjah (AE)
- Charles Darwin University (AU)
- Corresponding author
- Himeur, Yassine
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 118
- Pages
- 102746
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W4224940282
- Cited by
- 139
- References
- 16
- Citations by year
- 2026: 20 · 2025: 39 · 2024: 37 · 2023: 36 · 2022: 7
- FWCI
- 29.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers & Security (publishedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- recommender systems, privacy preservation, security (computing), trust, authentication, malicious attacks, fairness, bias, filter bubbles, blockchain, explainable AI, edge computing, federated learning, Internet of Things
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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