activity
20172023
most citedProving Differential Privacy with Shadow Execution

47 citations · 104 across the 12 of their papers we have counts for

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Showing cs.CRShow all

11 papers · 1 filter

cs.CR2023

A Floating-Point Secure Implementation of the Report Noisy Max with Gap Mechanism

Zeyu Ding, John Durrell, Daniel Kifer +5

The Noisy Max mechanism and its variations are fundamental private selection algorithms that are used to select items from a set of candidates (such as the most common diseases in…

cs.CR20221 cited

Answering Private Linear Queries Adaptively using the Common Mechanism

Yingtai Xiao, Guanhong Wang, Danfeng Zhang +1

When analyzing confidential data through a privacy filter, a data scientist often needs to decide which queries will best support their intended analysis. For example, an analyst m…

cs.CR2022

Reconstruction Attacks on Aggressive Relaxations of Differential Privacy

Prottay Protivash, John Durrell, Zeyu Ding +2

Differential privacy is a widely accepted formal privacy definition that allows aggregate information about a dataset to be released while controlling privacy leakage for individua…

cs.CR2022

Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism

Brian Karrer, Daniel Kifer, Arjun Wilkins +1

Sparse histogram methods can be useful for returning differentially private counts of items in large or infinite histograms, large group-by queries, and more generally, releasing a…

cs.CR2021

Towards a General-Purpose Dynamic Information Flow Policy

Peixuan Li, Danfeng Zhang

Noninterference offers a rigorous end-to-end guarantee for secure propagation of information. However, real-world systems almost always involve security requirements that change du…

cs.CR202110 cited

DPGen: Automated Program Synthesis for Differential Privacy

Yuxin Wang, Zeyu Ding, Yingtai Xiao +2

Differential privacy has become a de facto standard for releasing data in a privacy-preserving way. Creating a differentially private algorithm is a process that often starts with…