The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Anonymizing Classification Data for Privacy Preservation
Abstract
Classification is a fundamental problem in data analysis. Training a classifier requires accessing a large collection of data. Releasing person-specific data, such as customer data or patient records, may pose a threat to an individual's privacy. Even after removing explicit identifying information such as Name and SSN, it is still possible to link released records back to their identities by matching some combination of nonidentifying attributes such as {Sex, Zip, Birthdate}. A useful approach to combat such linking attacks, called k-anonymization, is anonymizing the linking attributes so that at least k released records match each value combination of the linking attributes. Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric. We argue that minimizing the distortion to the training data is not relevant to the classification goal that requires extracting the structure of predication on the "future" data. In this paper, we propose a k-anonymization solution for classification. Our goal is to find a k-anonymization, not necessarily optimal in the sense of minimizing data distortion, which preserves the classification structure. We conducted intensive experiments to evaluate the impact of anonymization on the classification on future data. Experiments on real-life data show that the quality of classification can be preserved even for highly restrictive anonymity requirements
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Cites
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L -diversity: Privacy beyond k -anonymity
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L-diversity: privacy beyond k-anonymity
Machanavajjhala, A., Gehrke, J., Kifer, D., & Venkitasubramaniam, M. (2006) · 22nd International Conference on Data Engineering (ICDE'06) · doi:10.1109/icde.2006.1 · Cited by 2,362
Protecting respondents identities in microdata release
Samarati, P. (2001) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.971193 · Cited by 2,324
ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s021848850200165x · Cited by 1,976
Data Privacy through Optimal k-Anonymization
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Mondrian Multidimensional K-Anonymity
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Incognito: efficient full-domain K-anonymity
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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-citationGiray, G. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.111031 · Cited by 187
Context-Aware Generative Adversarial Privacy
Huang, C., Kairouz, P., Chen, X., Sankar, L., & Rajagopal, R. (2017) · Entropy · doi:10.3390/e19120656 · Cited by 156
A Review of Anonymization for Healthcare Data
Olatunji, I. E., Rauch, J., Katzensteiner, M., & Khosla, M. (2024) · Big Data · doi:10.1089/big.2021.0169 · Cited by 101
Improved l-diversity: scalable anonymization approach for privacy preserving big data publishing
Mehta, B. B., & Rao, U. P. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2019.08.006 · Cited by 57
Metadata
- Authors
- Fung, B. C. M., Wang, K., & Yu, P. S.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2007-05
- Volume
- 19
- Issue
- 5
- Pages
- 711-725
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2118944554
- Cited by
- 349
- References
- 28
- FWCI
- 19.31
- Type
- journal article
- Language
- en
- Open access
- closed
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