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A Novel Self-Distillation Architecture to Defeat Membership Inference Attacks
Xinyu Tang · Saeed Mahloujifar · Liwei Song · Virat Shejwalkar · Amir Houmansadr · Prateek Mittal
Event URL: https://openreview.net/forum?id=eKkpcdSA52W »

Membership inference attacks are a key measure to evaluate privacy leakage in machine learning (ML) models, which aim to distinguish training members from non-members by exploiting differential behavior of the models on member and non-member inputs. We propose a new framework to train privacy-preserving models that induces similar behavior on member and non-member inputs to mitigate practical membership inference attacks. Our framework, called SELENA, has two major components. The first component and the core of our defense, called Split-AI, is a novel ensemble architecture for training. We prove that our Split-AI architecture defends against a large family of membership inference attacks, however, it is susceptible to new adaptive attacks. Therefore, we use a second component in our framework called Self-Distillation to protect against such stronger attacks, which (self-)distills the training dataset through our Split-AI ensemble and has no reliance on external public datasets. We perform extensive experiments on major benchmark datasets and the results show that our approach achieves a better trade-off between membership privacy and utility compared to previous defenses.

Author Information

Xinyu Tang (Princeton University)
Saeed Mahloujifar (Princeton)
Liwei Song (Princeton University)
Virat Shejwalkar (University of Massachusetts Amherst)
Amir Houmansadr (University of Massachusetts Amherst)
Prateek Mittal (Princeton University)

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