A probabilistic approach for disclosure risk assessment in statistical databases

Bice Cavallo () and Gerardo Canfora

Quality & Quantity: International Journal of Methodology, 2016, vol. 50, issue 2, 729-749

Abstract: In this paper, disclosure risk assessment in Statistical Databases is performed by means of a probabilistic approach; in particular, we consider the problem of auditing databases that support statistical sum/count/mean/max/min queries to protect the privacy of sensitive boolean data. We provide both a theoretical framework for evaluating the disclosure risk and a tool for its control and management. Copyright Springer Science+Business Media Dordrecht 2016

Keywords: Disclosure risk assessment; Privacy; Statistical databases; Bayesian network; Boolean data (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s11135-015-0173-5 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:50:y:2016:i:2:p:729-749

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-015-0173-5

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().