Review for NeurIPS paper: De-Anonymizing Text by Fingerprinting Language Generation

This paper generated a significant amount of discussion. SCIENTIFIC: Regarding the purely scientific aspects, the reviewers discussed about the novelty of the contribution. On the one hand, if one takes the point of view of the security community, the proposed attack and defense are known and the vulnerability is not surprising since any data-dependent accesses is prone to side-channel attacks. On the other hand, from the point of view of the machine learning community where these concerns are currently not well known, the paper presents very clearly a reasonable approach to start thinking about security of machine learning and NLP code using actual algorithms that text generation researchers and practitioners use. The paper can thus serve a useful cross-discipline discussion.