REVIEW 3 cited by
The Bounded Laplace Mechanism in Differential Privacy
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The Laplace mechanism is the workhorse of differential privacy, applied to many instances where numerical data is processed. However, the Laplace mechanism can return semantically impossible values, such as negative counts, due to its infinite support. There are two popular solutions to this: (i) bounding/capping the output values and (ii) bounding the mechanism support. In this paper, we show that bounding the mechanism support, while using the parameters of the pure Laplace mechanism, does not typically preserve differential privacy. We also present a robust method to compute the optimal mechanism parameters to achieve differential privacy in such a setting.
Forward citations
Cited by 3 Pith papers
-
PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization
PLRV-O replaces Gaussian noise in DP-SGD with a randomized-scale Laplace distribution and claims large accuracy gains at epsilon under 1, but the privacy accounting likely underestimates the true privacy loss due to s...
-
Diffprivlib: The IBM Differential Privacy Library
The paper presents Diffprivlib as the first unifying open-source Python library implementing differential privacy mechanisms and applications for data analytics and machine learning.
-
On Fair Ordering and Differential Privacy
This paper claims any additive-noise differential privacy mechanism can guarantee fair ordering in blockchain systems, a reduction that is stated but not rigorously proven.
Discussion (0). Continue with ORCID to comment.