MODELLING USER UNCERTAINTY FOR DISCLOSURE RISK AND DATA UTILITY

In this paper we show how a simple model that captures user uncertainty can be used to define suitable measures of disclosure risk and data utility. The model generalizes previous results of Duncan and Lambert.1 We present several examples to illustrate how the new measures can be used to implement existing optimality criteria for the choice of the best form of data release.

Morris H. Degroot

Degroot, Morris H.

Additive noise and multiplicative bias as disclosure limitation techniques for continuous microdata: A simulation study

This paper focuses on a combination of two disclosure limitation techniques, additive noise and multiplicative bias, and studies their efficacy in protecting confidentiality of continuous microdata. A Bayesian intruder model is extensively simulated in order to assess the performance of these disclosure limitation techniques as a function of key parameters like the variability amongst profiles in the original data, the amount of users prior information, the amount of bias and noise introduced in the data. The results of the simulation offer insight into the degree of vulnerability of data on continuous random variables and suggests some guidelines for effective protection measures.