World Scientific
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at customercare@wspc.com for any enquiries.
Special Issue on Computational Definitions of Privacy and AnonymityNo Access

STATISTICAL DEPENDENCE AS THE BASIS FOR A PRIVACY MEASURE FOR MICRODATA RELEASE

    https://doi.org/10.1142/S0218488512400296Cited by:3 (Source: Crossref)

    Government agencies and other organizations commonly release or share microdata for purposes of analysis. In many cases, microdata release needs to preserve the privacy of individuals and/or sensitive attributes. Current measures of privacy of released microdata are often based on empirical assessments of identity and value disclosure. The disadvantage of empirical assessments of privacy is that their results cannot be generalized with confidence across datasets or protection methods. While theoretical definitions of privacy are available for other methods of data release such as query-response output perturbation systems, they are unsuitable for the microdata release context. This study proposes a theoretical basis for measuring privacy in the microdata release context based on statistical dependence. Using this theoretical basis, we develop practical privacy measures that possess several desirable properties, including generalizability. We illustrate the conceptual benefits of this approach and also show that a privacy measure based on statistical dependence can be used effectively for assessing privacy in microdata