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    <titleInfo>
        <title>Differentially Private Instance Encoding against Privacy Attacks</title>
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    <name type="personal">
        <namePart type="given">Shangyu</namePart>
        <namePart type="family">Xie</namePart>
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    <name type="personal">
        <namePart type="given">Yuan</namePart>
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            <title>Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop</title>
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            <namePart type="given">Daphne</namePart>
            <namePart type="family">Ippolito</namePart>
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        <name type="personal">
            <namePart type="given">Liunian</namePart>
            <namePart type="given">Harold</namePart>
            <namePart type="family">Li</namePart>
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            <namePart type="given">Maria</namePart>
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            <namePart type="given">Danqi</namePart>
            <namePart type="family">Chen</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nianwen</namePart>
            <namePart type="family">Xue</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>TextHide was recently proposed to protect the training data via instance encoding in natural language domain. Due to the lack of theoretic privacy guarantee, such instance encoding scheme has been shown to be vulnerable against privacy attacks, e.g., reconstruction attack. To address such limitation, we revise the instance encoding scheme with differential privacy and thus provide a provable guarantee against privacy attacks. The experimental results also show that the proposed scheme can defend against privacy attacks while ensuring learning utility (as a trade-off).</abstract>
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    <identifier type="doi">10.18653/v1/2022.naacl-srw.22</identifier>
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        <date>2022-07</date>
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%0 Conference Proceedings
%T Differentially Private Instance Encoding against Privacy Attacks
%A Xie, Shangyu
%A Hong, Yuan
%Y Ippolito, Daphne
%Y Li, Liunian Harold
%Y Pacheco, Maria Leonor
%Y Chen, Danqi
%Y Xue, Nianwen
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
%D 2022
%8 July
%I Association for Computational Linguistics
%C Hybrid: Seattle, Washington + Online
%F xie-hong-2022-differentially
%X TextHide was recently proposed to protect the training data via instance encoding in natural language domain. Due to the lack of theoretic privacy guarantee, such instance encoding scheme has been shown to be vulnerable against privacy attacks, e.g., reconstruction attack. To address such limitation, we revise the instance encoding scheme with differential privacy and thus provide a provable guarantee against privacy attacks. The experimental results also show that the proposed scheme can defend against privacy attacks while ensuring learning utility (as a trade-off).
%R 10.18653/v1/2022.naacl-srw.22
%U https://aclanthology.org/2022.naacl-srw.22
%U https://doi.org/10.18653/v1/2022.naacl-srw.22
%P 172-180

Markdown (Informal)

[Differentially Private Instance Encoding against Privacy Attacks](https://aclanthology.org/2022.naacl-srw.22) (Xie & Hong, NAACL 2022)

• Differentially Private Instance Encoding against Privacy Attacks (Xie & Hong, NAACL 2022)

ACL

• Shangyu Xie and Yuan Hong. 2022. Differentially Private Instance Encoding against Privacy Attacks. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, pages 172–180, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.