Peer-Reviewed Journal Details
Mandatory Fields
Holohan N.;Leith D.;Mason O.
2016
October
Discrete Applied Mathematics
Differentially private response mechanisms on categorical data
Published
1 ()
Optional Fields
Data privacy Differential privacy Optimal mechanisms
211
86
98
© 2016 Elsevier B.V. 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.
0166-218X
10.1016/j.dam.2016.04.010
Grant Details