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.
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy
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abstract
We explore Reconstruction Robustness (ReRo), which was recently proposed as an upper bound on the success of data reconstruction attacks against machine learning models. Previous research has demonstrated that differential privacy (DP) mechanisms also provide ReRo, but so far, only asymptotic Monte Carlo estimates of a tight ReRo bound have been shown. Directly computable ReRo bounds for general DP mechanisms are thus desirable. In this work, we establish a connection between hypothesis testing DP and ReRo and derive closed-form, analytic or numerical ReRo bounds for the Laplace and Gaussian mechanisms and their subsampled variants.
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Covert Attacks on Machine Learning Training in Passively Secure MPC
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.