The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Aggregate Query Answering on Anonymized Tables
Abstract
Privacy is a serious concern when microdata need to be released for ad hoc analyses. The privacy goals of existing privacy protection approaches (e.g., k-anonymity and l-diversity) are suitable only for categorical sensitive attributes. Since applying them directly to numerical sensitive attributes (e.g., salary) may result in undesirable information leakage, we propose privacy goals to better capture the need of privacy protection for numerical sensitive attributes. Complementing the desire for privacy is the need to support ad hoc aggregate analyses over microdata. Existing generalization-based anonymization approaches cannot answer aggregate queries with reasonable accuracy. We present a general framework of permutation-based anonymization to support accurate answering of aggregate queries and show that, for the same grouping, permutation-based techniques can always answer aggregate queries more accurately than generalization-based approaches. We further propose several criteria to optimize permutations for accurate answering of aggregate queries, and develop efficient algorithms for each criterion.
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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
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
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335438 · Cited by 2,961
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
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
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
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
Incognito: efficient full-domain K-anonymity
LeFevre, K., DeWitt, D. J., & Ramakrishnan, R. (2005) · Proceedings of the 2005 ACM SIGMOD international conference on Management of data · doi:10.1145/1066157.1066164 · Cited by 1,070
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
Cited by
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451
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
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
Privacy: Theory meets Practice on the Map
Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., & Vilhuber, L. (2008) · 2008 IEEE 24th International Conference on Data Engineering · doi:10.1109/icde.2008.4497436 · Cited by 435
Metadata
- Authors
- Zhang, Q., Koudas, N., Srivastava, D., & Yu, T.
- Venue
- 2007 IEEE 23rd International Conference on Data Engineering
- Publisher
- IEEE
- Published
- 2007-04
- Pages
- 116-125
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W2166658587
- Cited by
- 357
- References
- 19
- FWCI
- 23.42
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhang, Q., Koudas, N., Srivastava, D., & Yu, T. (2007). Aggregate Query Answering on Anonymized Tables. 2007 IEEE 23rd International Conference on Data Engineering. https://doi.org/10.1109/icde.2007.367857
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