Optimal Noise Adding Mechanisms for Approximate Differential Privacy

115 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The Composition Theorem for Differential Privacy (2013, background): “In terms of variance-privacy trade-off for real-valued queries, the optimal noise-adding mechanism known as the staircase mechanism was introduced in [20].”
Differentially Private Uniformly Most Powerful Tests for Binomial Data (2018, methodology): “Related work on developing optimal DP mechanisms for general loss functions such as Geng and Viswanath (2016a) and Ghosh et al. (2009), give mechanisms that optimize symmetric convex loss functions, centered at a real statistic.”
Optimal Noise-Adding Mechanism in Additive Differential Privacy (2018, methodology): “Geng and Viswanath (2016b) derived the optimal -differentially privacy noise adding mechanism for single real-valued query function with arbitrary query sensitivity, and show that the optimal noise distribution has a staircase-shaped probability density function.”
The Optimal Noise-Adding Mechanism in Differential Privacy (2012, methodology): “We would like to point it out that the staircase mechanism has been extended to the multidimensional setting in [53], and the optimal mechanism is also studied in [54] for approximate differential privacy under a similar optimization framework.”