Venue: NeurIPS 2022 Workshop TSRML

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: 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.
review_title: Well-motivated though empirical results slightly weak

Decision: Accept