A comprehensive survey on local differential privacy
X Xiong, S Liu, D Li, Z Cai, X Niu - Security and Communication …, 2020 - Wiley Online Library
With the advent of the era of big data, privacy issues have been becoming a hot topic in
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …
Differentially private empirical risk minimization with non-convex loss functions
We study the problem of Empirical Risk Minimization (ERM) with (smooth) non-convex loss
functions under the differential-privacy (DP) model. Existing approaches for this problem …
functions under the differential-privacy (DP) model. Existing approaches for this problem …
On differentially private stochastic convex optimization with heavy-tailed data
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 data …
Stochastic Convex Optimization (SCO) on heavy-tailed data. The irregularity of such data …
Empirical risk minimization in non-interactive local differential privacy revisited
In this paper, we revisit the Empirical Risk Minimization problem in the non-interactive local
model of differential privacy. In the case of constant or low dimensions ($ p\ll n $), we first …
model of differential privacy. In the case of constant or low dimensions ($ p\ll n $), we first …
On sparse linear regression in the local differential privacy model
In this paper, we study the sparse linear regression problem under the Local Differential
Privacy (LDP) model. We first show that polynomial dependency on the dimensionality $ p …
Privacy (LDP) model. We first show that polynomial dependency on the dimensionality $ p …
Differentially private empirical risk minimization with smooth non-convex loss functions: A non-stationary view
In this paper, we study the Differentially Private Empirical Risk Minimization (DP-ERM)
problem with non-convex loss functions and give several upper bounds for the utility in …
problem with non-convex loss functions and give several upper bounds for the utility in …
Faster rates of private stochastic convex optimization
In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization
(DP-SCO) and provide excess population risks for some special classes of functions that are …
(DP-SCO) and provide excess population risks for some special classes of functions that are …
Estimating smooth glm in non-interactive local differential privacy model with public unlabeled data
In this paper, we study the problem of estimating smooth Generalized Linear Models (GLM)
in the Non-interactive Local Differential Privacy (NLDP) model. Different from its classical …
in the Non-interactive Local Differential Privacy (NLDP) model. Different from its classical …
Optimal Locally Private Nonparametric Classification with Public Data
In this work, we investigate the problem of public data assisted non-interactive Local
Differentially Private (LDP) learning with a focus on non-parametric classification. Under the …
Differentially Private (LDP) learning with a focus on non-parametric classification. Under the …
Truthful and privacy-preserving generalized linear models
This paper explores estimating Generalized Linear Models (GLMs) when agents are
strategic and privacy-conscious. We aim to design mechanisms that encourage truthful …
strategic and privacy-conscious. We aim to design mechanisms that encourage truthful …