Any decision about the release of microdata for public use is supported by estimation measures disclosure risk, most popular being number τ1 sample uniques that are also population uniques. In such a context, parametric and nonparametric partition-based models have been shown to have: i) strength leading estimators with desirable features, including ease implementation, computational efficiency...

An increase in quality and detail of publicly available databases increases the risk of disclosure of sensitive personal information contained in such databases. The goal of Statistical Disclosure Control (SDC) is to develop methodology that aims at minimizing disclosure risk while providing society with as much information as possible needed for valid statistical inference. The Post Randomizat...

In this paper we study both the level of Value-at-Risk (VaR) disclosure and the accuracy of the disclosed VaR figures for a sample of US and international commercial banks. To measure the level of VaR disclosures, we develop a VaR Disclosure Index that captures many different facets of market risk disclosure. Using panel data over the period 1996–2005, we find an overall upward trend in the qua...

The prevalence of categorical observations that are unique in a sample and also still unique in the population are usually taken as the measure of the overall risk of disclosure in the sample data. Samuels (1998) suggested adopting evolutionary processes and their associated urn models as a framework for estimating this prevalence. We re-examine his proposal and suggest several extensions that ...

Research has established that R&D-intensive firms are characterized by substantial future risk-adjusted stock returns. The reasons for this phenomenon and its policy implications, however, are widely debated. Some attribute the excess returns to investors‘ systematic undervaluation of R&D firms and argue for improved disclosure to mitigate the mispricing, while others claim that the excess retu...

With the growing focus on prevention in medicine, studies of how to describe risk have become increasing important. Recently, some researchers have argued against giving patients "comparative risk information," such as data about whether their baseline risk of developing a particular disease is above or below average. The concern is that giving patients this information will interfere with thei...

When patient data are shared for studying a specific disease, a privacy disclosure occurs as long as an individual is known to be in the shared data. Individuals in such specific disease data are thus subject to higher disclosure risk than those in datasets with different diseases. This problem has been overlooked in privacy research and practice. In this study, we analyze disclosure risks for ...