103 citations · 196 across the 8 of their papers we have counts for
9 papers
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…
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…
The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise
Zeyu Ding, Daniel Kifer, Sayed M. Saghaian N. E. +4
The permute-and-flip mechanism is a recently proposed differentially private selection algorithm that was shown to outperform the exponential mechanism. In this paper, we show that…
Free Gap Estimates from the Exponential Mechanism, Sparse Vector, Noisy Max and Related Algorithms
Zeyu Ding, Yuxin Wang, Yingtai Xiao +3
Private selection algorithms, such as the Exponential Mechanism, Noisy Max and Sparse Vector, are used to select items (such as queries with large answers) from a set of candidates…
Optimizing Fitness-For-Use of Differentially Private Linear Queries
Yingtai Xiao, Zeyu Ding, Yuxin Wang +2
In practice, differentially private data releases are designed to support a variety of applications. A data release is fit for use if it meets target accuracy requirements for each…
CheckDP: An Automated and Integrated Approach for Proving Differential Privacy or Finding Precise Counterexamples
Yuxin Wang, Zeyu Ding, Daniel Kifer +1
We propose CheckDP, the first automated and integrated approach for proving or disproving claims that a mechanism is differentially private. CheckDP can find counterexamples for me…
Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms
Zeyu Ding, Yuxin Wang, Danfeng Zhang +1
Noisy Max and Sparse Vector are selection algorithms for differential privacy and serve as building blocks for more complex algorithms. In this paper we show that both algorithms c…
Proving Differential Privacy with Shadow Execution
Yuxin Wang, Zeyu Ding, Guanhong Wang +2
Recent work on formal verification of differential privacy shows a trend toward usability and expressiveness -- generating a correctness proof of sophisticated algorithm while mini…
Detecting Violations of Differential Privacy
Zeyu Ding, Yuxin Wang, Guanhong Wang +2
The widespread acceptance of differential privacy has led to the publication of many sophisticated algorithms for protecting privacy. However, due to the subtle nature of this priv…