Data Privacy

Michele Bezzi, Sabrina Capitani di Vimercati, Sara Foresti, Giovanni Livraga, Stefano Paraboschi and Pierangela Samarati
Additional contact information
Michele Bezzi: SAP Labs France
Sabrina Capitani di Vimercati: Dip. di Tecnologie dell’Informazione
Sara Foresti: Dip. di Tecnologie dell’Informazione
Giovanni Livraga: Dip. di Tecnologie dell’Informazione
Stefano Paraboschi: Università degli Studi di Bergamo, DIIMM
Pierangela Samarati: Dip. di Tecnologie dell’Informazione

Chapter Chapter 8 in Privacy and Identity Management for Life, 2011, pp 157-179 from Springer

Abstract: Abstract In today’s globally interconnected society, a huge amount of data about individuals is collected, processed, and disseminated. Data collections often contain sensitive personally identifiable information that need to be adequately protected against improper disclosure. In this chapter, we describe novel informationtheoretical privacy metrics, necessary to measure the privacy degree guaranteed by a published dataset. We then illustrate privacy protection techniques, based on fragmentation, that can be used to protect sensitive data and sensitive associations among them.

Keywords: Mutual Information; Data Privacy; Relation Schema; Data Owner; Query Evaluation (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:sprchp:978-3-642-20317-6_8

Ordering information: This item can be ordered from
http://www.springer.com/9783642203176

DOI: 10.1007/978-3-642-20317-6_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().