Shypd.aiThe largest open index of AI toolsml_privacy_meter is an open-source library to audit data privacy in statistical and machine learning algorithms. It enables data protection impact assessment based on state-of-the-art membership inference attacks.
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ml_privacy_meter is an open-source Python library designed to audit data privacy in a wide range of statistical and machine learning algorithms, including classification, regression, computer vision, and natural language processing. It helps researchers and practitioners assess the privacy risks associated with their models by enabling data protection impact assessment based on state-of-the-art membership inference attacks. The tool supports various auditing methodologies, such as membership inference, range membership inference, and dataset usage cardinality inference, and can also audit differential privacy lower bounds. It is compatible with diverse datasets like CIFAR10, Purchase, and AG News, and models such as CNN, AlexNet, and GPT-2. Users can extend its functionality to other HuggingFace datasets and transformers, or integrate custom training scripts.
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