To do or not to do: the dilemma of disclosing anonymized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On disclosure risk analysis of anonymized itemsets in the presence of prior knowledge
ACM Transactions on Knowledge Discovery from Data (TKDD)
Can attackers learn from samples?
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
An information theoretic privacy and utility measure for data sanitization mechanisms
Proceedings of the second ACM conference on Data and Application Security and Privacy
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Statistical database protection is a part of information security which tries to prevent published statistical information (tables, individual records) from disclosing the contribution of specific respondents. This paper shows how to use information-theoretic concepts to measure disclosure risk for tabular data. The proposed disclosure risk measure is compatible with a broad class of disclosure protection methods and can be extended for computing disclosure risk for a set of linked tables.