Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk

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Author

Elisa Bertino, Guy Lebanon, Monica Scannapieco, Mohamed R. Fouad

Entry type

book

Abstract

An important issue any organization or individual has to face when managing data containing sensitive information, is the risk that can be incurred when releasing such data. Even though data may be sanitized, before being released, it is still possible for an adversary to reconstruct the original data by using additional information that may be available, for example, from other data sources. To date, however, no comprehensive approach exists to quantify such risks. In this paper we develop a framework, based on statistical decision theory, to assess the relationship between the disclosed data and the resulting privacy risk. We relate our framework with the k-anonymity disclosure method; we make the assumptions behind k-anonymity explicit, quantify them, and extend them in several natural directions.

URL

http://www.springerlink.com/content/12256752pr244621/

Booktitle

Privacy in Statistical Databases

Key alpha

Bertino

Pages

217-232

Publisher

Springer Berlin / Heidelberg

Series

Lecture Notes in Computer Science

Volume

4302

Affiliation

Purdue University

Publication Date

0000-00-00

Copyright

2006

Isbn

978-3-540-49330-3

Issn

0302-9743 (Print) 1611-3349 (Online)

BibTex-formatted data

To refer to this entry, you may select and copy the text below and paste it into your BibTex document. Note that the text may not contain all macros that BibTex supports.

@Book{ Bertino,
	title = " Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk",
	author = "Elisa Bertino, Guy Lebanon, Monica Scannapieco, Mohamed R. Fouad",
	booktitle = "Privacy in Statistical Databases",
	pages = "217-232",
	publisher = "Springer Berlin / Heidelberg",
	series = "Lecture Notes in Computer Science",
	volume = "4302",
	abstract = "An important issue any organization or individual has to face when managing data containing sensitive information, is the risk that can be incurred when releasing such data. Even though data may be sanitized, before being released, it is still possible for an adversary to reconstruct the original data by using additional information that may be available, for example, from other data sources. To date, however, no comprehensive approach exists to quantify such risks. In this paper we develop a framework, based on statistical decision theory, to assess the relationship between the disclosed data and the resulting privacy risk. We relate our framework with the k-anonymity disclosure method; we make the assumptions behind k-anonymity explicit, quantify them, and extend them in several natural directions.",
	affiliation = "Purdue University",
	copyright = "2006",
	isbn = "978-3-540-49330-3",
	issn = "0302-9743 (Print) 1611-3349 (Online)",
	url = "http://www.springerlink.com/content/12256752pr244621/",
}