In security-sensitive applications, the success of machine learning depends on a thorough
vetting of their resistance to adversarial data. In one pertinent, well-motivated attack …

Abstract Machine learning models trained on private datasets have been shown to leak their
private data. Recent work has found that the average data point is rarely leaked---it is often …

There is growing recognition that machine learning (ML) exposes new security and privacy
vulnerabilities in software systems, yet the technical community's understanding of the …

Machine learning is increasingly used in securitycritical applications, such as autonomous
driving, face recognition, and malware detection. Most learning methods, however, have not …

Written by leading researchers, this complete introduction brings together all the theory and
tools needed for building robust machine learning in adversarial environments. Discover …

Machine learning (ML) has achieved huge success in recent years, but is also vulnerable to
various attacks. In this article, we concentrate on membership inference attacks and propose …

This article deals with adversarial attacks towards deep learning systems for Natural
Language Processing (NLP), in the context of privacy protection. We study a specific type of …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

How much does a machine learning algorithm leak about its training data, and why?
Membership inference attacks are used as an auditing tool to quantify this leakage. In this …

Given access to a machine learning model, can an adversary reconstruct the model's
training data? This work studies this question from the lens of a powerful informed adversary …