Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?

Sorami Hisamoto, Matt Post, Kevin Duh


Abstract
Data privacy is an important issue for “machine learning as a service” providers. We focus on the problem of membership inference attacks: Given a data sample and black-box access to a model’s API, determine whether the sample existed in the model’s training data. Our contribution is an investigation of this problem in the context of sequence-to-sequence models, which are important in applications such as machine translation and video captioning. We define the membership inference problem for sequence generation, provide an open dataset based on state-of-the-art machine translation models, and report initial results on whether these models leak private information against several kinds of membership inference attacks.
Anthology ID:
2020.tacl-1.4
Volume:
Transactions of the Association for Computational Linguistics, Volume 8
Month:
Year:
2020
Address:
Venue:
TACL
SIG:
Publisher:
Note:
Pages:
49–63
URL:
https://www.aclweb.org/anthology/2020.tacl-1.4
DOI:
10.1162/tacl_a_00299
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
https://www.aclweb.org/anthology/2020.tacl-1.4.pdf