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).
From the training portion, we designate 10% of the points as the forget set Df (4,500 points), and use the remaining 40,500 points as the retain set Dr
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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).