10 citations · 12 across the 6 of their papers we have counts for
7 papers
Function Privatization in the Local Model
Yuting Liang, Tian Shu, Ke Yi
We study the problem of privately releasing functions, with a particular focus on curves, which are images of continuous functions on some finite interval. Many types of data exist…
Adaptive Privacy Budgeting
Yuting Liang, Ke Yi
We study the problem of adaptive privacy budgeting under generalized differential privacy. Consider the setting where each user holds a tuple $x_i\in U:=U_1\times \dotsb…
Smooth Sensitivity for Geo-Privacy
Yuting Liang, Ke Yi
Suppose each user holds a private value in some metric space , and an untrusted data analyst wishes to compute for some function $f :…
Concentrated Geo-Privacy
Yuting Liang, Ke Yi
This paper proposes concentrated geo-privacy (CGP), a privacy notion that can be considered as the counterpart of concentrated differential privacy (CDP) for geometric data. Compar…
Differentially Private Covariance Revisited
Wei Dong, Yuting Liang, Ke Yi
In this paper, we present two new algorithms for covariance estimation under concentrated differential privacy (zCDP). The first algorithm achieves a Frobenius error of $\tilde{O}(…
Instance-optimal Mean Estimation Under Differential Privacy
Ziyue Huang, Yuting Liang, Ke Yi
Mean estimation under differential privacy is a fundamental problem, but worst-case optimal mechanisms do not offer meaningful utility guarantees in practice when the global sensit…
Towards Robust Deep Learning with Ensemble Networks and Noisy Layers
Yuting Liang, Reza Samavi
In this paper we provide an approach for deep learning that protects against adversarial examples in image classification-type networks. The approach relies on two mechanisms:1) a…