Parity-based inference control for multi-dimensional range sum queries
Journal of Computer Security
Disclosure Analysis and Control in Statistical Databases
ESORICS '08 Proceedings of the 13th European Symposium on Research in Computer Security: Computer Security
An efficient online auditing approach to limit private data disclosure
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Self-enforcing Private Inference Control
ProvSec '09 Proceedings of the 3rd International Conference on Provable Security
New paradigm of inference control with trusted computing
Proceedings of the 21st annual IFIP WG 11.3 working conference on Data and applications security
Disclosure analysis for two-way contingency tables
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
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The fundamental problem for inference control in data cubes is how to efficiently calculate the lower and upper bounds for each cell value given the aggregations of cell values over multiple dimensions. In this paper, we provide the first practical solution for estimating exact bounds in two-dimensional irregular data cubes (i.e., data cubes in which certain cell values are known to a snooper). Our results imply that the exact bounds cannot be obtained by a direct application of the Fréchet bounds in some cases. We then propose a new approach to improve the classic Fréchet bounds for any high-dimensional data cube in the most general case. The proposed approach improves upon the Fréchet bounds in the sense that it gives bounds that are at least as tight as those computed by Fréchet, yet is simpler in terms of time complexity. Based on our solutions to the fundamental problem, we discuss two security applications, privacy protection of released data and fine-grained access control and auditing.