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Holohan, Naoise, 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: | 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) |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/10019 |
| 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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