arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2303.03053cs.CL
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具有隐私保护文本重写的敏感数据众包

Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting

DIPF, Leibniz Institute for Research and Information in Education(莱布尼茨教育研究与信息研究所(DIPF));summetix GmbH(Summetix有限公司);Technical University of Darmstadt(达姆施塔特工业大学)

Nina Mouhammad, Johannes Daxenberger, Benjamin Schiller, Ivan Habernal

摘要:

NLP中的大多数任务都需要标记数据。由于可扩展性的原因,数据标签通常在众包平台上完成。但是,只有在不包含隐私相关信息的情况下,才能在公共平台上发布数据。文本数据通常包含敏感信息,如人名或位置。在这项工作中,我们调查如何删除个人身份信息(PII)以及应用差分隐私(DP)重写可以使文本与隐私相关的信息被用于众包。我们发现,在众包之前进行DP重写可以保护隐私,同时仍然可以为某些任务和数据提供良好的标签质量。在所有检查的任务中,去除PII导致良好的标签质量,但是,没有给出隐私保证。

英文摘要:

Most tasks in NLP require labeled data. Data labeling is often done on crowdsourcing platforms due to scalability reasons. However, publishing data on public platforms can only be done if no privacy-relevant information is included. Textual data often contains sensitive information like person names or locations. In this work, we investigate how removing personally identifiable information (PII) as well as applying differential privacy (DP) rewriting can enable text with privacy-relevant information to be used for crowdsourcing. We find that DP-rewriting before crowdsourcing can preserve privacy while still leading to good label quality for certain tasks and data. PII-removal led to good label quality in all examined tasks, however, there are no privacy guarantees given.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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