Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …

Privacy and Byzantine resilience are two indispensable requirements for a federated
learning (FL) system. Although there have been extensive studies on privacy and Byzantine …

Generalized linear models (GLMs) such as logistic regression are among the most widely
used arms in data analyst's repertoire and often used on sensitive datasets. A large body of …

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 …

We explore the power of the hybrid model of differential privacy (DP), in which some users
desire the guarantees of the local model of DP and others are content with receiving the …

Local differential privacy (LDP) is a model where users send privatized data to an untrusted
central server whose goal it to solve some data analysis task. In the non-interactive version …

Deep learning models for NLP tasks are prone to variants of privacy attacks. To prevent
privacy leakage, researchers have investigated word-level perturbations, relying on the …

We consider stochastic convex optimization for heavy-tailed data with the guarantee of
being differentially private (DP). Most prior works on differentially private stochastic convex …

It has been shown recently that many non-convex objective/loss functions in machine
learning are known to be strict saddle. This means that finding a second-order stationary …

Recently, many machine learning and statistical models such as non-linear regressions, the
Single Index, Multi-index, Varying Coefficient Index Models and Two-layer Neural Networks …