Practical data-swapping: the first steps
ACM Transactions on Database Systems (TODS)
Private lives and public policies: confidentiality and accessibility of government statistics
Private lives and public policies: confidentiality and accessibility of government statistics
A modified random perturbation method for database security
ACM Transactions on Database Systems (TODS)
Linear queries in statistical databases
ACM Transactions on Database Systems (TODS)
Assessing global disclosure risk in masked microdata
Proceedings of the 2004 ACM workshop on Privacy in the electronic society
On tracker attacks in health grids
Proceedings of the 2006 ACM symposium on Applied computing
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In this paper, we introduce three microdata disclosure risk measures (minimal, maximal and weighted) for sampling disclosure control method. The minimal disclosure risk measure represents the percentage of records that can be correctly identified by an intruder based on prior knowledge of key attribute values. The maximal disclosure risk measure considers the risk associated with probabilistic record linkage for records that are not unique in the masked microdata. The weighted disclosure risk measure allows the data owner to compute the risk of disclosure based on weights associated with different clusters of records. The weights allow a flexible specification of the relative importance of varying cluster sizes in probabilistic record linkage. We show that weighted disclosure risk measure is always between the values of minimal and maximal disclosure risk measures, and moreover for certain values of the weights, the weighted disclosure risk measure is equal to one of the other two measures. Using simulated medical data in our experiments, we show that the proposed disclosure risk measures perform as expected in real-life situations.