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A Note On Interpreting Canary Exposure

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arxiv 2306.00133 v2 pith:LRGEDXZK submitted 2023-05-31 cs.CR cs.LG

Matthew Jagielski This is my paper · ORCID
classification cs.CRcs.LG
keywords canaryexposurenoteprivacyattacksauditcarlinidifferential
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Canary exposure, introduced in Carlini et al. is frequently used to empirically evaluate, or audit, the privacy of machine learning model training. The goal of this note is to provide some intuition on how to interpret canary exposure, including by relating it to membership inference attacks and differential privacy.

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Cited by 1 Pith paper

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  1. Covert Attacks on Machine Learning Training in Passively Secure MPC

    cs.CR 2025-05 conditional novelty 7.0 of 10

    An active adversary can exploit additive error injection in passively secure MPC training to poison models, amplify membership inference, reduce fairness, and reconstruct exact training data.

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