Assessing Global Disclosure Risk in Masked Microdata

Assessing Global Disclosure Risk in Masked Microdata. Truta, T., Fotouhi, F., & Barth-Jones, D. In Proceedings of the Workshop on Privacy in the Electronic Society (WPES), pages 85-93, 10, 2004. ACM Press.

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In this paper, we introduce a general framework for microdata and three disclosure risk measures (minimal, maximal and weighted). We classify the attributes from a given microdata in two different ways: based on their potential identification utility and based on the order relation that exists in their domain of value. We define inversion and change factors that allow data users to quantify the magnitude of masking modification incurred for values of a key attribute. The disclosure risk measures are based on these inversion and change factors, and can be computed for any specific disclosure control method, or any combination of methods applied in succession to a given microdata. Using simulated medical data in our experiments, we show that the proposed disclosure risk measures perform as expected in real-life situations.

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