Holohan, Naoise and Leith, Douglas J. and Mason, Oliver (2014) Differential Privacy in Metric Spaces: Numerical, Categorical and Functional Data Under the One Roof. Technical Report. arXiv.org.

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Abstract

We study Differential Privacy in the abstract setting of Probability on metric spaces. Numerical, categorical and functional data can be handled in a uniform manner in this setting. We demonstrate how mechanisms based on data sanitisation and those that rely on adding noise to query responses fit within this framework. We prove that once the sanitisation is differentially private, then so is the query response for any query. We show how to construct sanitisations for high-dimensional databases using simple 1-dimensional mechanisms. We also provide lower bounds on the expected error for differentially private sanitisations in the general metric space setting. Finally, we consider the question of sufficient sets for differ- ential privacy and show that for relaxed differential privacy, any algebra generating the Borel ơ-algebra is a sufficient set for relaxed differential privacy.

Item Type:	Monograph (Technical Report)
Additional Information:	This Hamilton Institute Tech Report is available at arXiv:1402.6124 . This is the preprint version of the article published in Information Sciences (ISSN 0020-0255) Volume 305, 1 June 2015, Pages 256–268 doi:10.1016/j.ins.2015.01.021
Keywords:	Differential Privacy; Metric Space; Categorical Data; Func- tional Data; Data Sanitisation;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	5950
Depositing User:	Professsor Douglas Leith
Date Deposited:	11 Mar 2015 17:03
Publisher:	arXiv.org
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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