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    <titleInfo>
        <title>Privacy-preserving Neural Representations of Text</title>
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    <name type="personal">
        <namePart type="given">Maximin</namePart>
        <namePart type="family">Coavoux</namePart>
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    <name type="personal">
        <namePart type="given">Shashi</namePart>
        <namePart type="family">Narayan</namePart>
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        <namePart type="given">Shay</namePart>
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        <namePart type="family">Cohen</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>This article deals with adversarial attacks towards deep learning systems for Natural Language Processing (NLP), in the context of privacy protection. We study a specific type of attack: an attacker eavesdrops on the hidden representations of a neural text classifier and tries to recover information about the input text. Such scenario may arise in situations when the computation of a neural network is shared across multiple devices, e.g. some hidden representation is computed by a user’s device and sent to a cloud-based model. We measure the privacy of a hidden representation by the ability of an attacker to predict accurately specific private information from it and characterize the tradeoff between the privacy and the utility of neural representations. Finally, we propose several defense methods based on modified training objectives and show that they improve the privacy of neural representations.</abstract>
    <identifier type="citekey">coavoux-etal-2018-privacy</identifier>
    <identifier type="doi">10.18653/v1/D18-1001</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Privacy-preserving Neural Representations of Text
%A Coavoux, Maximin
%A Narayan, Shashi
%A Cohen, Shay B.
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F coavoux-etal-2018-privacy
%X This article deals with adversarial attacks towards deep learning systems for Natural Language Processing (NLP), in the context of privacy protection. We study a specific type of attack: an attacker eavesdrops on the hidden representations of a neural text classifier and tries to recover information about the input text. Such scenario may arise in situations when the computation of a neural network is shared across multiple devices, e.g. some hidden representation is computed by a user’s device and sent to a cloud-based model. We measure the privacy of a hidden representation by the ability of an attacker to predict accurately specific private information from it and characterize the tradeoff between the privacy and the utility of neural representations. Finally, we propose several defense methods based on modified training objectives and show that they improve the privacy of neural representations.
%R 10.18653/v1/D18-1001
%U https://aclanthology.org/D18-1001
%U https://doi.org/10.18653/v1/D18-1001
%P 1-10

Markdown (Informal)

[Privacy-preserving Neural Representations of Text](https://aclanthology.org/D18-1001) (Coavoux et al., EMNLP 2018)

• Privacy-preserving Neural Representations of Text (Coavoux et al., EMNLP 2018)

ACL

• Maximin Coavoux, Shashi Narayan, and Shay B. Cohen. 2018. Privacy-preserving Neural Representations of Text. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1–10, Brussels, Belgium. Association for Computational Linguistics.