Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

We propose new differential privacy solutions for when external invariants and integer
constraints are simultaneously enforced on the data product. These requirements arise in …

Multi-label propagation algorithms (MLPAs) aim to find vertex communities in a complex
network or a cloud system by propagating and updating vertex labels, which have been …

Differential privacy (DP) has achieved remarkable results in the field of privacy-preserving
machine learning. However, existing DP frameworks do not satisfy all the conditions for …

Pr (T (X)∈·) at every dataset X in the data universe D. Derivatives measure change in output
per change in input. How do we measure change? 2. Divergence dX on the data input …

After an election, should election officials release an electronic record of each ballot? The
release of such cast vote records could bolster the legitimacy of the certified result. But it may …

Differential privacy (DP) quantifies privacy loss by analyzing noise injected into output
statistics. For non-trivial statistics, this noise is necessary to ensure finite privacy loss …

Differential privacy (DP) is a mathematical standard for assessing the privacy provided by a
data-release mechanism. We provide formulations of pure $\epsilon $-differential privacy …

In this paper, we provide two views of constrained differential private (DP) mechanisms. The
first one is as belief revision. A constrained DP mechanism is obtained by standard …

Canonical noise distributions and private hypothesis tests Page 1 The Annals of Statistics
2023, Vol. 51, No. 2, 547–572 https://doi.org/10.1214/23-AOS2259 © Institute of Mathematical …