47 citations · 104 across the 11 of their papers we have counts for
16 papers
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…
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…
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…
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…
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…
Understanding TEE Containers, Easy to Use? Hard to Trust
Weijie Liu, Hongbo Chen, XiaoFeng Wang +4
As an emerging technique for confidential computing, trusted execution environment (TEE) receives a lot of attention. To better develop, deploy, and run secure applications on a TE…