Broadening the scope of differential privacy using metrics

Konstantinos Chatzikokolakis, Miguel E. Andrés, Nicolás Emilio Bordenabe, Catuscia Palamidessi

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

83 Citations (Scopus)

Abstract

Differential Privacy is one of the most prominent frameworks used to deal with disclosure prevention in statistical databases. It provides a formal privacy guarantee, ensuring that sensitive information relative to individuals cannot be easily inferred by disclosing answers to aggregate queries. If two databases are adjacent, i.e. differ only for an individual, then the query should not allow to tell them apart by more than a certain factor. This induces a bound also on the distinguishability of two generic databases, which is determined by their distance on the Hamming graph of the adjacency relation. In this paper we explore the implications of differential privacy when the indistinguishability requirement depends on an arbitrary notion of distance. We show that we can naturally express, in this way, (protection against) privacy threats that cannot be represented with the standard notion, leading to new applications of the differential privacy framework. We give intuitive characterizations of these threats in terms of Bayesian adversaries, which generalize two interpretations of (standard) differential privacy from the literature. We revisit the well-known results stating that universally optimal mechanisms exist only for counting queries: We show that, in our extended setting, universally optimal mechanisms exist for other queries too, notably sum, average, and percentile queries. We explore various applications of the generalized definition, for statistical databases as well as for other areas, such that geolocation and smart metering.
Original languageEnglish
Title of host publicationPrivacy enhancing technologies
Subtitle of host publication13th international symposium, PETS 2013, Bloomington, IN, USA, July 10-12, 2013, proceedings
EditorsEmiliano De Cristofaro, Matthew Wright
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Pages82-102
Number of pages21
ISBN (Print)9783642390760
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventInternational Symposium on Privacy Enhancing Technologies (13th : 2013) - Bloomington, IN
Duration: 10 Jul 201312 Jul 2013

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume7981
ISSN (Print)0302-9743

Conference

ConferenceInternational Symposium on Privacy Enhancing Technologies (13th : 2013)
CityBloomington, IN
Period10/07/1312/07/13

Cite this

Chatzikokolakis, K., Andrés, M. E., Bordenabe, N. E., & Palamidessi, C. (2013). Broadening the scope of differential privacy using metrics. In E. De Cristofaro, & M. Wright (Eds.), Privacy enhancing technologies: 13th international symposium, PETS 2013, Bloomington, IN, USA, July 10-12, 2013, proceedings (pp. 82-102). (Lecture notes in computer science; Vol. 7981). Berlin: Springer, Springer Nature. https://doi.org/10.1007/978-3-642-39077-7_5