Security of statistical databases: multidimensional transformation
ACM Transactions on Database Systems (TODS)
A security machanism for statistical database
ACM Transactions on Database Systems (TODS)
A General Additive Data Perturbation Method for Database Security
Management Science
Secure Databases: Constraints, Inference Channels, and Monitoring Disclosures
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
The inference problem: a survey
ACM SIGKDD Explorations Newsletter
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
IEEE Transactions on Knowledge and Data Engineering
Privacy Preserving Data Classification with Rotation Perturbation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Individual Privacy and Organizational Privacy in Business Analytics
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Practical Inference Control for Data Cubes
IEEE Transactions on Dependable and Secure Computing
Disclosure Analysis and Control in Statistical Databases
ESORICS '08 Proceedings of the 13th European Symposium on Research in Computer Security: Computer Security
Disclosure risk in dynamic two-dimensional contingency tables (extended abstract)
ICISS'06 Proceedings of the Second international conference on Information Systems Security
| Hi-index | 0.00 |
The 2D (two-dimensional) contingency tables have been used in many aspects of our daily life. In practice, errors may be incurred when generating or editing such a table hence the data contained by the table could be inaccurate. Even so, it is still possible for a knowledgeable snooper who may have acquired the information of error distributions to decipher some private information from a released table. This paper investigates the estimation of privacy disclosure probability for contingency tables with inaccurate data based on Fréchet bounds and proposes two optimization solutions for the control of privacy disclosure so as to preserve private information. Our estimation of privacy disclosure probability and the optimization solutions are also applicable to error-free tables which can be regarded as a special case where there are no errors. The effectiveness of the solutions is verified by rigorous experiments.