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        <title>TextHide: Tackling Data Privacy in Language Understanding Tasks</title>
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        <namePart type="given">Yangsibo</namePart>
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    <abstract>An unsolved challenge in distributed or federated learning is to effectively mitigate privacy risks without slowing down training or reducing accuracy. In this paper, we propose TextHide aiming at addressing this challenge for natural language understanding tasks. It requires all participants to add a simple encryption step to prevent an eavesdropping attacker from recovering private text data. Such an encryption step is efficient and only affects the task performance slightly. In addition, TextHide fits well with the popular framework of fine-tuning pre-trained language models (e.g., BERT) for any sentence or sentence-pair task. We evaluate TextHide on the GLUE benchmark, and our experiments show that TextHide can effectively defend attacks on shared gradients or representations and the averaged accuracy reduction is only 1.9%. We also present an analysis of the security of TextHide using a conjecture about the computational intractability of a mathematical problem.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.123</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T TextHide: Tackling Data Privacy in Language Understanding Tasks
%A Huang, Yangsibo
%A Song, Zhao
%A Chen, Danqi
%A Li, Kai
%A Arora, Sanjeev
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F huang-etal-2020-texthide
%X An unsolved challenge in distributed or federated learning is to effectively mitigate privacy risks without slowing down training or reducing accuracy. In this paper, we propose TextHide aiming at addressing this challenge for natural language understanding tasks. It requires all participants to add a simple encryption step to prevent an eavesdropping attacker from recovering private text data. Such an encryption step is efficient and only affects the task performance slightly. In addition, TextHide fits well with the popular framework of fine-tuning pre-trained language models (e.g., BERT) for any sentence or sentence-pair task. We evaluate TextHide on the GLUE benchmark, and our experiments show that TextHide can effectively defend attacks on shared gradients or representations and the averaged accuracy reduction is only 1.9%. We also present an analysis of the security of TextHide using a conjecture about the computational intractability of a mathematical problem.
%R 10.18653/v1/2020.findings-emnlp.123
%U https://aclanthology.org/2020.findings-emnlp.123
%U https://doi.org/10.18653/v1/2020.findings-emnlp.123
%P 1368-1382

Markdown (Informal)

[TextHide: Tackling Data Privacy in Language Understanding Tasks](https://aclanthology.org/2020.findings-emnlp.123) (Huang et al., Findings 2020)

• TextHide: Tackling Data Privacy in Language Understanding Tasks (Huang et al., Findings 2020)

ACL

• Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, and Sanjeev Arora. 2020. TextHide: Tackling Data Privacy in Language Understanding Tasks. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1368–1382, Online. Association for Computational Linguistics.