Abstract Algorithms such as Differentially Private SGD enable training machine learning
models with formal privacy guarantees. However, because these guarantees hold with …

(ε, δ) differential privacy has seen increased adoption recently, especially in private machine
learning applications. While this privacy definition allows provably limiting the amount of …

Privacy-preserving machine learning aims to train models on private data without leaking
sensitive information. Differential privacy (DP) is considered the gold standard framework for …

Differential privacy (DP) aims to confer data processing systems with inherent privacy
guarantees, offering strong protections for personal data. But DP's approach to privacy …

Empirical defenses for machine learning privacy forgo the provable guarantees of
differential privacy in the hope of achieving higher utility while resisting realistic adversaries …

The private collection of multiple statistics from a population is a fundamental statistical
problem. One possible approach to realize this is to rely on the local model of differential …

During the COVID-19 pandemic, the use of mobile phone data for monitoring human
mobility patterns has become increasingly common, both to study the impact of travel …

In this article, we present a detailed review of current practices and state-of-the-art
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …

Differential privacy is a popular privacy-enhancing technology that has been deployed both
in industry and government agencies. Unfortunately, existing explanations of differential …

This publication describes differential privacy—a mathematical framework that quantifies
privacy risk to individuals as a consequence of data collection and subsequent data release …