Peer-Reviewed Journal Details
Mandatory Fields
Holohan, N;Leith, DJ;Mason, O
2015
June
Information Sciences
Differential privacy in metric spaces: Numerical, categorical and functional data under the one roof
Published
12 ()
Optional Fields
305
256
268
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 differential privacy and show that for relaxed differential privacy, any algebra generating the Borel sigma-algebra is a sufficient set for relaxed differential privacy. (C) 2015 Elsevier Inc. All rights reserved.
NEW YORK
0020-0255
10.1016/j.ins.2015.01.021
Grant Details