The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Privacy: Theory meets Practice on the Map
Abstract
In this paper, we propose the first formal privacy analysis of a data anonymization process known as the synthetic data generation, a technique becoming popular in the statistics community. The target application for this work is a mapping program that shows the commuting patterns of the population of the United States. The source data for this application were collected by the U.S. Census Bureau, but due to privacy constraints, they cannot be used directly by the mapping program. Instead, we generate synthetic data that statistically mimic the original data while providing privacy guarantees. We use these synthetic data as a surrogate for the original data. We find that while some existing definitions of privacy are inapplicable to our target application, others are too conservative and render the synthetic data useless since they guard against privacy breaches that are very unlikely. Moreover, the data in our target application is sparse, and none of the existing solutions are tailored to anonymize sparse data. In this paper, we propose solutions to address the above issues.
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Cites
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
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
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
Samarati, P., & Sweeney, L. (2018) · doi:10.1184/r1/6625469 · Cited by 1,326
Data Privacy through Optimal k-Anonymization
Bayardo, R. J., & Agrawal, R. (2005) · 21st International Conference on Data Engineering (ICDE'05) · doi:10.1109/icde.2005.42 · Cited by 1,131
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
Smooth sensitivity and sampling in private data analysis
Nissim, K., Raskhodnikova, S., & Smith, A. (2007) · Proceedings of the thirty-ninth annual ACM symposium on Theory of computing · doi:10.1145/1250790.1250803 · Cited by 974
Security-control methods for statistical databases: a comparative study
Adam, N. R., & Worthmann, J. C. (1989) · ACM Computing Surveys · doi:10.1145/76894.76895 · Cited by 969
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
Self-citation
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
Routes for breaching and protecting genetic privacy
Erlich, Y., & Narayanan, A. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3723 · Cited by 458
Broadening the Scope of Differential Privacy Using Metrics
Chatzikokolakis, K., Andrés, M. E., Bordenabe, N. E., & Palamidessi, C. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-39077-7_5 · Cited by 340
The U.S. Census Bureau Adopts Differential Privacy
Abowd, J. M. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3226070 · Cited by 292
Self-citationRankin, D., Black, M., Bond, R., Wallace, J., Mulvenna, M., & Epelde, G. (2020) · JMIR Medical Informatics · doi:10.2196/18910 · Cited by 169
Metadata
- Authors
- Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., & Vilhuber, L.
- Venue
- 2008 IEEE 24th International Conference on Data Engineering
- Publisher
- IEEE
- Published
- 2008-04
- Pages
- 277-286
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W4255574744
- Cited by
- 435
- References
- 30
- FWCI
- 11.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., & Vilhuber, L. (2008). Privacy: Theory meets Practice on the Map. 2008 IEEE 24th International Conference on Data Engineering. https://doi.org/10.1109/icde.2008.4497436
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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.
