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Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression
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Citations
Cited by
L -diversity: Privacy beyond k -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
Differential Privacy: A Survey of Results
Dwork, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-79228-4_1 · Cited by 3,542
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
Information revelation and privacy in online social networks
Gross, R., & Acquisti, A. (2005) · Proceedings of the 2005 ACM workshop on Privacy in the electronic society · doi:10.1145/1102199.1102214 · Cited by 2,028
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
Metadata
- Authors
- Samarati, P., & Sweeney, L.
- Published
- 2018-06-30
- OpenAlex
- W6922108917
- Cited by
- 1,326
- References
- 0
- FWCI
- 51.91
- Type
- journal article
- Language
- en
- Open access
- green
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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. https://app.cochat.ai/papers/10.1184/r1/6625469
- The paper
- Samarati, P., & Sweeney, L. (2018). Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. https://doi.org/10.1184/r1/6625469
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
