Reza Shokri

Privacy-preserving deep learning

2015

Deep learning based on artificial neural networks is a very popular approach to modeling, classifying, and recognizing complex data such as images, speech, and text. The unprecedented accuracy of deep learning methods has turned them …

Ultimate Power of Inference Attacks: Privacy Risks of High-Dimensional Models.

2019 · arXiv (Cornell University)

Models leak information about their training data. This enables attackers to infer sensitive information about their training sets, notably determine if a data sample was part of the model's training set. The existing works empirically …

Model Explanations with Differential Privacy

2020 · arXiv (Cornell University)

Black-box machine learning models are used in critical decision-making domains, giving rise to several calls for more algorithmic transparency. The drawback is that model explanations can leak information about the training data and the explanation …

Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks

2022

The wide adoption and application of Masked language models (MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy vulnerabilities. Prior attempts at measuring leakage of MLMs via membership …

Low-Cost High-Power Membership Inference Attacks

2023 · arXiv (Cornell University)

Membership inference attacks aim to detect if a particular data point was used in training a model. We design a novel statistical test to perform robust membership inference attacks (RMIA) with low computational overhead. We …