103 citations · 197 across the 16 of their papers we have counts for
17 papers
DP4SQL: Differentially Private SQL with Flexible Privacy Policies
Andrew Cascio, KinChin Tong, Daniel Kifer +2
The plausible deniability model of differential privacy for single-table datasets is well-understood. However, applying differential privacy to relational databases is much trickie…
The Trap of Trajectory: Towards Understanding and Mitigating Spurious Correlations in Agentic Memory
Luoxi Tang, Rupali Rajendra Vaje, Yuqiao Meng +5
Agentic memory enables LLMs to persist information beyond a single context window and reuse it in later decisions, but it also introduces a new vulnerability: spurious correlations…
Accurate and Scalable Matrix Mechanisms via Divide and Conquer
Guanlin He, Yingtai Xiao, Jiamu Bai +4
Matrix mechanisms are often used to provide unbiased differentially private query answers when publishing statistics or creating synthetic data. Recent work has developed matrix me…
Scalable Learning of Multivariate Distributions via Coresets
Zeyu Ding, Katja Ickstadt, Nadja Klein +2
Efficient and scalable non-parametric or semi-parametric regression analysis and density estimation are of crucial importance to the fields of statistics and machine learning. Howe…
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…
ResidualPlanner+: a scalable matrix mechanism for marginals and beyond
Guanlin He, Yingtai Xiao, Levent Toksoz +3
Noisy marginals are a common form of confidentiality protecting data release and are useful for many downstream tasks such as contingency table analysis, construction of Bayesian n…
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…
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…
Canonical Barsotti-Tate Groups of Finite Level
Zeyu Ding
Let be an algebraically closed field of characteristic . Let be such that . Let be a -divisible group of codimension and dimension…
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…
Dimensions of automorphism group schemes of finite level truncations of -cyclic -crystals
Zeyu Ding, Xiao Xiao
Let be an -cyclic -crystal over an algebraically closed field defined by a permutation and a set of prescribed Hodge slopes. We prove comb…
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…