466 citations · 1.6k across the 73 of their papers we have counts for
8 papers · 1 filter
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…
Fast Private Adaptive Query Answering for Large Data Domains
Miguel Fuentes, Brett Mullins, Yingtai Xiao +3
Privately releasing marginals of a tabular dataset is a foundational problem in differential privacy. However, state-of-the-art mechanisms suffer from a computational bottleneck wh…
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…
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…
Differentially Private Hierarchical Count-of-Counts Histograms
Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer +2
We consider the problem of privately releasing a class of queries that we call hierarchical count-of-counts histograms. Count-of-counts histograms partition the rows of an input ta…