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        <title>Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?</title>
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    <name type="personal">
        <namePart type="given">Sorami</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00299</identifier>
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        <date>2020</date>
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%0 Journal Article
%T Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?
%A Hisamoto, Sorami
%A Post, Matt
%A Duh, Kevin
%J Transactions of the Association for Computational Linguistics
%D 2020
%V 8
%I MIT Press
%C Cambridge, MA
%F hisamoto-etal-2020-membership
%X 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.
%R 10.1162/tacl_a_00299
%U https://aclanthology.org/2020.tacl-1.4
%U https://doi.org/10.1162/tacl_a_00299
%P 49-63

Markdown (Informal)

[Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?](https://aclanthology.org/2020.tacl-1.4) (Hisamoto et al., TACL 2020)

• Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System? (Hisamoto et al., TACL 2020)

ACL

• Sorami Hisamoto, Matt Post, and Kevin Duh. 2020. Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?. Transactions of the Association for Computational Linguistics, 8:49–63.