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Privacy Auditing with One (1) Training Run
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We propose a scheme for auditing differentially private machine learning systems with a single training run. This exploits the parallelism of being able to add or remove multiple training examples independently. We analyze this using the connection between differential privacy and statistical generalization, which avoids the cost of group privacy. Our auditing scheme requires minimal assumptions about the algorithm and can be applied in the black-box or white-box setting.
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Auditing of Unlearning Algorithms
An auditor based on membership inference attacks computes valid lower bounds on the unlearning parameter ε, empirically separating certified unlearning methods (small bounds) from heuristic ones (large bounds).
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