Holohan, Naoise and Leith, Douglas J. and Mason, Oliver (2016) Differentially private response mechanisms on categorical data. Discrete Applied Mathematics, 211. pp. 86-98. ISSN 0166-218X

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Abstract

We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for differential privacy we obtain necessary and sufficient conditions for differential privacy, a tight lower bound on the maximal expected error of a discrete mechanism and a characterisation of the optimal mechanism which minimises the maximal expected error within the class of mechanisms considered.

Item Type:	Article
Keywords:	Data privacy; Differential privacy; Optimal mechanisms;
Academic Unit:	Faculty of Science and Engineering > Mathematics and Statistics Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	10019
Identification Number:	https://doi.org/10.1016/j.dam.2016.04.010
Depositing User:	Oliver Mason
Date Deposited:	27 Sep 2018 15:32
Journal or Publication Title:	Discrete Applied Mathematics
Publisher:	Elsevier
Refereed:	Yes
Funders:	Science Foundation Ireland (SFI)
URI:	https://www.sciencedirect.com
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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