Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
A modified random perturbation method for database security
ACM Transactions on Database Systems (TODS)
Security of random data perturbation methods
ACM Transactions on Database Systems (TODS)
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Disclosure risk measures for microdata
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
An epistemic framework for privacy protection in database linking
Data & Knowledge Engineering
K-anonymization incremental maintenance and optimization techniques
Proceedings of the 2007 ACM symposium on Applied computing
Granulation as a privacy protection mechanism
Transactions on rough sets VII
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In this paper, we first introduce minimal, maximal and weighted disclosure risk measures for microaggregation disclosure control method. Our disclosure risk measures are more applicable to real-life situations, compute the overall disclosure risk, and are not linked to a target individual. After defining those disclosure risk measures, we then introduce an information loss measure for microaggregation. The minimal disclosure risk measure represents the percentage of records, which 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. Information loss measure, introduced in this paper, extends the existing measure proposed by Domingo-Ferrer, and captures the loss of information at record level as well as from the statistical integrity point of view. Using simulated medical data in our experiments, we show that the proposed disclosure risk and information loss measures perform as expected in real-life situations.