From: NeurIPS.cc/2022/Workshop/TSRML/Program_Chairs
title: Paper Decision
decision: Accept

From: NeurIPS.cc/2022/Workshop/TSRML/Paper4/Reviewer_KS5D
title: Well-motivated though empirical results slightly weak
summary: This paper studies a new membership inference attack, which is the most common privacy attack, via the adversarial examples. Consequently a new defense is developed.
strengths: This paper is well-written, with enlightening observation of "peaks of confidence" and good argument about adversarial path. The resulting algorithms are clear and moderately effective. Overall the contribution is solid.
weaknesses: The empirical performance is not strong and computational cost may be concerning. Also, given that the motivation is empirical, it would be desirable to look at MNIST instead of synthetic dataset.
overall_recommendation: I recommend acceptance of this work.
overall_rating: 8: Top 50% of accepted papers, clear accept
review_confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct