Bias and Variance of Post-processing in Differential Privacy
- URL: http://arxiv.org/abs/2010.04327v1
- Date: Fri, 9 Oct 2020 02:12:54 GMT
- Title: Bias and Variance of Post-processing in Differential Privacy
- Authors: Keyu Zhu, Pascal Van Hentenryck, Ferdinando Fioretto
- Abstract summary: Post-processing immunity is a fundamental property of differential privacy.
It is often argued that post-processing may introduce bias and increase variance.
This paper takes a first step towards understanding the properties of post-processing.
- Score: 53.29035917495491
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Post-processing immunity is a fundamental property of differential privacy:
it enables the application of arbitrary data-independent transformations to the
results of differentially private outputs without affecting their privacy
guarantees. When query outputs must satisfy domain constraints, post-processing
can be used to project the privacy-preserving outputs onto the feasible region.
Moreover, when the feasible region is convex, a widely adopted class of
post-processing steps is also guaranteed to improve accuracy. Post-processing
has been applied successfully in many applications including census
data-release, energy systems, and mobility. However, its effects on the noise
distribution is poorly understood: It is often argued that post-processing may
introduce bias and increase variance. This paper takes a first step towards
understanding the properties of post-processing. It considers the release of
census data and examines, both theoretically and empirically, the behavior of a
widely adopted class of post-processing functions.
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