A systematic review of privacy techniques in recommendation systems
Integrity signals
Registry
Cites
Statistics
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?
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 [8]
This architecture, also known as a user-centralized architecture, assures better privacy than centralized RS, as users actively manage their data during the recommendation process.
“This architecture, also known as a user-centralized architecture [128], assures better privacy than centralized RS, as users actively manage their data during the recommendation process [8].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [8]
ZKP requires the user to prove that their actions adhere to a specified protocol, ensuring honest behaviour while maintaining privacy.
“This concept requires the user to prove that their actions adhere to a specified protocol, ensuring honest behaviour while maintaining privacy [8].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [8]
Cites
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
Privacy-preserving data publishing: A survey of recent developments
Fung, B. C. M., Wang, K., Chen, R., & Yu, P. S. (2010) · ACM Computing Surveys · doi:10.1145/1749603.1749605 · Cited by 1,704 · cited 1× · see citing works
“Corporations today collect huge digital personal and nonpersonal information from internet users, which has created tremendous benefits for knowledge-based decision-making [1, 2].”
BackgroundAbdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030072 · Cited by 796 · see citing works
Slokom, M., Hanjalic, A., & Larson, M. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102722 · Cited by 31 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651
- Authors
- Ogunseyi, T. B., Avoussoukpo, C. B., & Jiang, Y.
- Affiliations
- Yibin University (CN)
- Corresponding author
- Ogunseyi, Taiwo Blessing
- Venue
- International Journal of Information Security
- Published
- 2023-06-05
- Volume
- 22
- Issue
- 6
- Pages
- 1651-1664
- ISSN
- 1615-5262 (ISSN-L)
- 1615-5270
- OpenAlex
- W4379388878
- Cited by
- 27
- References
- 4
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 13
- FWCI
- 0.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Information Security (publishedVersion)
- Funders
- Yibin 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
- user privacy, privacy-preserving techniques, information overload, user data protection, open problems
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
