11 citations · 21 across the 15 of their papers we have counts for
15 papers
Geometry of Sensitivity: Twice Sampling and Hybrid Clipping in Differential Privacy with Optimal Gaussian Noise and Application to Deep Learning
Hanshen Xiao, Jun Wan, Srinivas Devadas
We study the fundamental problem of the construction of optimal randomization in Differential Privacy. Depending on the clipping strategy or additional properties of the processing…
Differentially Private Deep Learning with ModelMix
Hanshen Xiao, Jun Wan, Srinivas Devadas
Training large neural networks with meaningful/usable differential privacy security guarantees is a demanding challenge. In this paper, we tackle this problem by revisiting the two…
PAC Privacy: Automatic Privacy Measurement and Control of Data Processing
Hanshen Xiao, Srinivas Devadas
We propose and study a new privacy definition, termed Probably Approximately Correct (PAC) Privacy. PAC Privacy characterizes the information-theoretic hardness to recover sensitiv…
On the Foundation of Sparse Sensing (Part II): Diophantine Sampling and Array Configuration
Hanshen Xiao, Beining Zhou, Guoqiang Xiao
In the second part of the series papers, we set out to study the algorithmic efficiency of sparse sensing. Stemmed from co-prime sensing, we propose a generalized framework, termed…
On the Foundation of Sparse Sensing (Part I): Necessary and Sufficient Sampling Theory and Robust Remaindering Problem
Hanshen Xiao, Yaowen Zhang, Guoqiang Xiao
In the first part of the series papers, we set out to answer the following question: given specific restrictions on a set of samplers, what kind of signal can be uniquely represent…
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data
Lijie Hu, Shuo Ni, Hanshen Xiao +1
As one of the most fundamental problems in machine learning, statistics and differential privacy, Differentially Private Stochastic Convex Optimization (DP-SCO) has been extensivel…
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
Di Wang, Hanshen Xiao, Srini Devadas +1
In this paper, we consider the problem of designing Differentially Private (DP) algorithms for Stochastic Convex Optimization (SCO) on heavy-tailed data. The irregularity of such d…
Statistical Robust Chinese Remainder Theorem for Multiple Numbers
Hanshen Xiao, Nan Du, Zhikang T. Wang +1
Generalized Chinese Remainder Theorem (CRT) is a well-known approach to solve ambiguity resolution related problems. In this paper, we study the robust CRT reconstruction for multi…
From Co-prime to the Diophantine Equation Based Sparse Sensing
Hanshen Xiao, Guoqiang Xiao
With a careful design of sample spacings either in temporal and spatial domain, co-prime sensing can reconstruct the autocorrelation at a significantly denser set of points based o…
Local Differential Privacy in Decentralized Optimization
Hanshen Xiao, Yu Ye, Srinivas Devadas
Privacy concerns with sensitive data are receiving increasing attention. In this paper, we study local differential privacy (LDP) in interactive decentralized optimization. By cons…
Statistical Robust Chinese Remainder Theorem for Multiple Numbers: Wrapped Gaussian Mixture Model
Nan Du, Zhikang Wang, Hanshen Xiao
Generalized Chinese Remainder Theorem (CRT) has been shown to be a powerful approach to solve the ambiguity resolution problem. However, with its close relationship to number theor…
On Solving Ambiguity Resolution with Robust Chinese Remainder Theorem for Multiple Numbers
Hanshen Xiao, Guoqiang Xiao
Chinese Remainder Theorem (CRT) is a powerful approach to solve ambiguity resolution related problems such as undersampling frequency estimation and phase unwrapping which are wide…
Robustness in Chinese Remainder Theorem
Hanshen Xiao, Yufeng Huang, Yu Ye +1
Chinese Remainder Theorem (CRT) has been widely studied with its applications in frequency estimation, phase unwrapping, coding theory and distributed data storage. Since tradition…
New Residue Arithmetic Based Barrett Algorithms, Part II: Modular Polynomial Computations
Hari K Garg, Hanshen Xiao
In this paper, we derive a new computational algorithm for Barrett technique for modular polynomial multiplication, termed BA-P. BA-P is then applied to a new residue arithmetic ba…
New Residue Arithmetic Based Barrett Algorithms, Part I: Modular Integer Computations
Hari K. Garg, Hanshen Xiao
In this paper, we derive new computational techniques for residue number systems (RNS) based Barrett algorithm (BA). The focus of the work is an algorithm that carries out the enti…