Concrete mathematics: a foundation for computer science
Concrete mathematics: a foundation for computer science
Primal-Dual RNC Approximation Algorithms for Set Cover and Covering Integer Programs
SIAM Journal on Computing
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A fine-grained access control system for XML documents
ACM Transactions on Information and System Security (TISSEC)
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Secure Databases: Constraints, Inference Channels, and Monitoring Disclosures
IEEE Transactions on Knowledge and Data Engineering
A Secure Publishing Service for Digital Libraries of XML Documents
ISC '01 Proceedings of the 4th International Conference on Information Security
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
On privacy preservation against adversarial data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Controlling access to published data using cryptography
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Secure XML publishing without information leakage in the presence of data inference
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Consistency policies for dynamic information systems with declassification flows
ICISS'11 Proceedings of the 7th international conference on Information Systems Security
Anonymity and privacy in distributed early warning systems
CRITIS'10 Proceedings of the 5th international conference on Critical Information Infrastructures Security
Reconstruction attack through classifier analysis
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
| Hi-index | 0.00 |
In many data-publishing applications, the data owner needs to protect sensitive information pertaining to individuals. Meanwhile, certain information is required to be published. The sensitive information could be considered as leaked, if an adversary can infer the real value of a sensitive entry with a high confidence. In this paper we study how to protect sensitive data when an adversary can do inference attacks using association rules derived from the data. We formulate the inference attack model, and develop complexity results on computing a safe partial table. We classify the general problem into subcases based on the requirements of publishing information, and propose the corresponding algorithms for finding a safe partial table to publish. We have conducted an empirical study to evaluate these algorithms on real data.