103 citations · 301 across the 19 of their papers we have counts for
4 papers · 1 filter
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…
Demystifying Arch-hints for Model Extraction: An Attack in Unified Memory System
Zhendong Wang, Xiaoming Zeng, Xulong Tang +3
The deep neural network (DNN) models are deemed confidential due to their unique value in expensive training efforts, privacy-sensitive training data, and proprietary network chara…
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…