Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …

Recent years have witnessed remarkable successes of machine learning in various
applications. However, machine learning models suffer from a potential risk of leaking …

We show that every approximately differentially private learning algorithm (possibly
improper) for a class H with Littlestone dimension d requires Ω (log*(d)) examples. As a …

We prove that every concept class with finite Littlestone dimension can be learned by an
(approximate) differentially-private algorithm. This answers an open question of Alon et …

Corporations, organizations, and governments have collected, digitized, and stored
information in digital forms since the invention of computers, and the speed of such data …

We consider learning problems where the training set consists of two types of examples:
private and public. The goal is to design a learning algorithm that satisfies differential privacy …

We investigate the direct-sum problem in the context of differentially private PAC learning:
What is the sample complexity of solving $ k $ learning tasks simultaneously under …

Let H be a binary-labeled concept class. We prove that H can be PAC learned by an
(approximate) differentially private algorithm if and only if it has a finite Littlestone dimension …

The equivalence of realizable and agnostic learnability is a fundamental phenomenon in
learning theory. With variants ranging from classical settings like PAC learning and …

The problem of learning threshold functions is a fundamental one in machine learning.
Classical learning theory implies sample complexity of O (ξ− 1 log (1/β))(for generalization …