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.
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