Did you know that over 70 million of Dota2 players have their in-game data freely
accessible? What if such data is used in malicious ways? This paper is the first to investigate …

Users in various web and mobile applications are vulnerable to attribute inference attacks, in
which an attacker leverages a machine learning classifier to infer a target user's private …

With an increase in low-cost machine learning APIs, advanced machine learning models
may be trained on private datasets and monetized by providing them as a service. However …

Models can expose sensitive information about their training data. In an attribute inference
attack, an adversary has partial knowledge of some training records and access to a model …

USENIX Security '22 Technical Sessions | USENIX Sign In Conferences Attend Registration
Information Registration Discounts Terms and Conditions Grant Opportunities Venue, Hotel …

Machine learning (ML) models have been deployed for high-stakes applications, eg,
healthcare and criminal justice. Prior work has shown that ML models are vulnerable to …

Machine learning algorithms, when applied to sensitive data, pose a distinct threat to
privacy. A growing body of prior work demonstrates that models produced by these …

We propose new privacy attacks to infer attributes (eg, locations, occupations, and interests)
of online social network users. Our attacks leverage seemingly innocent user information …

We propose new privacy attacks to infer attributes (eg, locations, occupations, and interests)
of online social network users. Our attacks leverage seemingly innocent user information …

Increasing use of ML technologies in privacy-sensitive domains such as medical diagnoses,
lifestyle predictions, and business decisions highlights the need to better understand if these …