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10/15/2021
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
Membership inference attacks are a key measure to evaluate privacy leaka...
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07/08/2020
A Critical Evaluation of Open-World Machine Learning
Open-world machine learning (ML) combines closed-world models trained on...
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05/01/2020
Universal Adversarial Attacks with Natural Triggers for Text Classification
Recent work has demonstrated the vulnerability of modern text classifier...
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03/24/2020
Systematic Evaluation of Privacy Risks of Machine Learning Models
Machine learning models are prone to memorizing sensitive data, making t...
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03/09/2020
Towards Probabilistic Verification of Machine Unlearning
Right to be forgotten, also known as the right to erasure, is the right ...
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05/24/2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
The arms race between attacks and defenses for machine learning models h...
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05/05/2019
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples
A large body of recent work has investigated the phenomenon of evasion a...
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12/21/2017