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ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning
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When building machine learning models using sensitive data, organizations should ensure that the data processed in such systems is adequately protected. For projects involving machine learning on personal data, Article 35 of the GDPR mandates it to perform a Data Protection Impact Assessment (DPIA). In addition to the threats of illegitimate access to data through security breaches, machine learning models pose an additional privacy risk to the data by indirectly revealing about it through the model predictions and parameters. Guidances released by the Information Commissioner's Office (UK) and the National Institute of Standards and Technology (US) emphasize on the threat to data from models and recommend organizations to account for and estimate these risks to comply with data protection regulations. Hence, there is an immediate need for a tool that can quantify the privacy risk to data from models. In this paper, we focus on this indirect leakage about training data from machine learning models. We present ML Privacy Meter, a tool that can quantify the privacy risk to data from models through state of the art membership inference attack techniques. We discuss how this tool can help practitioners in compliance with data protection regulations, when deploying machine learning models.
Forward citations
Cited by 5 Pith papers
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Cascading and Proxy Membership Inference Attacks
CMIA cascades conditional shadow training to exploit membership dependencies, and PMIA uses proxy data to approximate Bayesian membership odds, both substantially outperforming prior MIAs in low false-positive regimes.
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Auditing Fairness-Privacy Trade-offs: Subpopulation-Level Effects of Fairness-Enhancing Algorithms
Fairness-enhancing algorithms do not uniformly change membership-inference privacy risk; the effect depends on model architecture, subgroup size, and mitigation strategy, and DP's utility costs fall unevenly across subgroups.
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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study
Quantizers can be ranked by privacy using r_Q, a rate constant built from the loss gap and variance of low-loss quantized checkpoints along the training trajectory.
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Maturity Framework for Enhancing Machine Learning Quality
A quality score and five-level maturity framework for ML systems, open-sourced and rolled out at Booking.com to track and improve ML quality.
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Securing AI Systems: A Guide to Known Attacks and Impacts
A practitioner-oriented review that organizes known adversarial attacks on predictive and generative AI systems into eleven types mapped to confidentiality, integrity, and availability impacts.
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