The Staircase Mechanism in Differential Privacy
Authors:
- 23 April 2015
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Selected Topics in Signal Processing
- Vol. 9 (7) , 1176-1184
Abstract
Adding Laplacian noise is a standard approach in differential privacy to sanitize numerical data before releasing it. In this paper, we propose an alternative noise adding mechanism: the staircase mechanism, which is a geometric mixture of uniform random variables. The staircase mechanism can replace the Laplace mechanism in each instance in the literature and for the same level of differential privacy, the performance in each instance improves; the improvement is particularly stark in medium-low privacy regimes. We show that the staircase mechanism is the optimal noise adding mechanism in a universal context, subject to a conjectured technical lemma (which we also prove to be true for one and two dimensional data).Keywords
This publication has 36 references indexed in Scilit:
- Analyzing Graphs with Node Differential Privacy2013
- Functional mechanismProceedings of the VLDB Endowment, 2012
- Private Data Release via Learning Thresholds2012
- Iterative Constructions and Private Data Release2012
- What Can We Learn Privately?SIAM Journal on Computing, 2011
- Privacy integrated queriesCommunications of the ACM, 2010
- Differentially Private Combinatorial Optimization2010
- Differential Privacy: A Survey of Results2008
- Smooth sensitivity and sampling in private data analysis2007
- Calibrating Noise to Sensitivity in Private Data Analysis2006