The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Anonymizing Tables
Overview
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Citations
Cites
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335438 · Cited by 2,961
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335438 · Cited by 1,719
Efficient Private Matching and Set Intersection
Freedman, M. J., Nissim, K., & Pinkas, B. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24676-3_1 · Cited by 1,266
Privacy Preserving Data Mining
Lindell, Y., & Pinkas, B. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44598-6_3 · Cited by 1,077
On the design and quantification of privacy preserving data mining algorithms
Agrawal, D., & Aggarwal, C. C. (2001) · Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/375551.375602 · Cited by 1,006
Revealing information while preserving privacy
Dinur, I., & Nissim, K. (2003) · Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/773153.773173 · Cited by 980
On the complexity of optimal K-anonymity
Meyerson, A., & Williams, R. (2004) · Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/1055558.1055591 · Cited by 821
Limiting privacy breaches in privacy preserving data mining
Evfimievski, A., Gehrke, J., & Srikant, R. (2003) · Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/773153.773174 · Cited by 815
Generalizing data to provide anonymity when disclosing information (abstract)
Samarati, P., & Sweeney, L. (1998) · Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems · doi:10.1145/275487.275508 · Cited by 779
Cited by
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
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
Gedik, B., & Liu, L. (2008) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2007.1062 · Cited by 837
A General Survey of Privacy-Preserving Data Mining Models and Algorithms
Aggarwal, C. C., & Yu, P. S. (2008) · Advances in Database Systems · doi:10.1007/978-0-387-70992-5_2 · Cited by 469
M-invariance: towards privacy preserving re-publication of dynamic datasets
Xiao, X., & Tao, Y. (2007) · Proceedings of the 2007 ACM SIGMOD international conference on Management of data · doi:10.1145/1247480.1247556 · Cited by 466
Aggregate Query Answering on Anonymized Tables
Zhang, Q., Koudas, N., Srivastava, D., & Yu, T. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367857 · Cited by 357
Metadata
- Authors
- Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., & Zhu, A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2004
- Pages
- 246-258
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2912642709
- Cited by
- 292
- References
- 13
- FWCI
- 10.84
- Type
- conference paper
- Language
- en
- Open access
- closed
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 ›
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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: Anonymizing Tables. https://app.cochat.ai/papers/10.1007/978-3-540-30570-5_17
- The paper
- Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., & Zhu, A. (2004). Anonymizing Tables. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-30570-5_17
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.
