The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Abstract
The contingency table is a work horse of official statistics, the format of reported data for the US Census, Bureau of Labor Statistics, and the Internal Revenue Service. In many settings such as these privacy is not only ethically mandated, but frequently legally as well. Consequently there is an extensive and diverse literature dedicated to the problems of statistical disclosure control in contingency table release. However, all current techniques for reporting contingency tables fall short on at leas one of privacy, accuracy, and consistency (among multiple released tables). We propose a solution that provides strong guarantees for all three desiderata simultaneously.
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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
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
Self-citation
Cited by
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
Self-citationPrivacy-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
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194
(2011) · PubMed · Cited by 990
How Much Is Enough? Choosing ε for Differential Privacy
Lee, J., & Clifton, C. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-24861-0_22 · Cited by 201
SafePath: Differentially-private publishing of passenger trajectories in transportation systems
Al-Hussaeni, K., Fung, B. C. M., Iqbal, F., Dagher, G. G., & Park, E. G. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.07.007 · Cited by 52
Duan, Y., Liu, J., Ming, X., Jin, W., Song, Z., & Peng, X. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-35678-0_3 · Cited by 3
Metadata
- Authors
- Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., & Talwar, K.
- Venue
- Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
- Publisher
- ACM
- Published
- 2007-06-11
- Pages
- 273-282
- OpenAlex
- W2123733729
- Cited by
- 468
- References
- 2
- FWCI
- 14.96
- Type
- conference paper
- Language
- en
- Open access
- closed
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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.
