A Practical Privacy-Preserving Algorithm for Document Data

Tomoaki Mimoto, S. Kiyomoto, K. Kitamura, A. Miyaji
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引用次数: 1

Abstract

A huge number of documents such as news articles, public reports, and personal essays has been released on websites and social media. Once documents including privacy-sensitive information are published, the risk of privacy breaches increases; thus, documents should be carefully checked before publication. In many cases, human experts redact or sanitize documents before publishing; however, this approach is sometimes inefficient with regard to its cost and accuracy. Furthermore, critical privacy risks may remain in the documents. In this paper, we present a generalized adversary model and apply it to document data. This paper devises an attack algorithm for documents, which uses a web search engine, and proposes a privacy-preserving algorithm against the attacks. We evaluate the privacy risks for real accident reports from schools and court documents. As experiments using the real reports, we show that human-sanitized documents still include privacy risks, and our proposal would contribute to risk reduction.
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一种实用的文档数据隐私保护算法
在网站和社交媒体上发布了大量的新闻文章、公开报道、个人论文等文件。一旦包含隐私敏感信息的文件被公布,隐私泄露的风险就会增加;因此,文件在发表前应仔细检查。在许多情况下,人类专家在发布之前对文档进行编辑或消毒;然而,这种方法在成本和准确性方面有时效率低下。此外,关键的隐私风险可能仍然存在于文档中。在本文中,我们提出了一个广义的对手模型,并将其应用于文档数据。本文设计了一种基于web搜索引擎的文档攻击算法,并提出了一种针对攻击的隐私保护算法。我们评估来自学校和法庭文件的真实事故报告的隐私风险。通过使用真实报告的实验,我们发现人工消毒文档仍然存在隐私风险,我们的建议将有助于降低风险。
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