The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Abstract
Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first presentan optimal global-recoding method for the (α, k)-anonymity problem. Next we propose a local-recoding algorithm which is more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general case.
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Cites
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
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
Self-citationThe SAIL Databank: building a national architecture for e-health research and evaluation
Ford, D. V., Jones, K. H., Verplancke, J.-P., Lyons, R. A., John, G., Brown, G., Brooks, C. J., Thompson, S., Bodger, O., Couch, T., et al. (2009) · BMC Health Services Research · doi:10.1186/1472-6963-9-157 · Cited by 559
Anonymizing Classification Data for Privacy Preservation
Fung, B. C. M., Wang, K., & Yu, P. S. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.1015 · Cited by 349
Self-citationTechnical Privacy Metrics: A Systematic Survey
Wagner, I., & Eckhoff, D. (2019) · ACM Computing Surveys · doi:10.1145/3168389 · Cited by 346
Protecting Privacy Using k-Anonymity
El Emam, K., & Dankar, F. K. (2008) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2716 · Cited by 333
Metadata
- Authors
- Wong, R. C.-W., Li, J., Fu, A. W.-C., & Wang, K.
- Venue
- Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2006-08-20
- Pages
- 754-759
- OpenAlex
- W2009331946
- Cited by
- 625
- References
- 1
- FWCI
- 27.37
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wong, R. C.-W., Li, J., Fu, A. W.-C., & Wang, K. (2006). (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1150402.1150499
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