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 information-theoretical 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.
Data privacy / M. Bezzi, S. De Capitani di Vimercati, S. Foresti, G. Livraga, S. Paraboschi, P. Samarati - In: Privacy and identity management for life / [a cura di] J. Camenisch, S. Fischer-Hubner, K. Rannenberg. - Berlin : Springer, 2011. - ISBN 9783642203169. - pp. 157-179 [10.1007/978-3-642-20317-6_8]
Data privacy
S. De Capitani di VimercatiSecondo
;S. Foresti;G. Livraga;P. SamaratiUltimo
2011
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 information-theoretical 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.
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