Privacy Risk of Document Data and a Countermeasure Framework

• Item type: Journal(1)
• 公開日: 2021-12-15
• タイトル: Privacy Risk of Document Data and a Countermeasure Framework
• タイトル / 言語: en
• タイトル: Privacy Risk of Document Data and a Countermeasure Framework
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:デジタル社会の情報セキュリティとトラスト] privacy, document data, sanitization
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Advanced Telecommunications Research Institute International
• 著者所属: Advanced Telecommunications Research Institute International
• 著者所属: KDDI Research
• 著者所属: National Institute of Advanced Industrial Science and Technology
• 著者所属: Osaka University／Japan Advanced Institute of Science and Technology
• 著者所属(英): en
• 著者所属(英): Advanced Telecommunications Research Institute International
• 著者所属(英): en
• 著者所属(英): Advanced Telecommunications Research Institute International
• 著者所属(英): en
• 著者所属(英): KDDI Research
• 著者所属(英): en
• 著者所属(英): National Institute of Advanced Industrial Science and Technology
• 著者所属(英): en
• 著者所属(英): Osaka University / Japan Advanced Institute of Science and Technology
• 著者名: Tomoaki, Mimoto; Masayuki, Hashimoto; Shinsaku, Kiyomoto; Koji, Kitamura; Atsuko, Miyaji
• 著者名(英): Tomoaki, Mimoto; Masayuki, Hashimoto; Shinsaku, Kiyomoto; Koji, Kitamura; Atsuko, Miyaji
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: A huge number of documents such as news articles, public reports, and personal essays have been released on websites and social media. Once documents containing privacy-sensitive information are published, the risk of privacy breaches increases, thus requiring very careful review of documents prior to publication. In many cases, human experts redact or sanitize documents before publishing them; however, this approach can be inefficient with regard to cost and accuracy. Furthermore, such measures do not guarantee that critical privacy risks are eliminated from the documents. In this paper, we present a generalized adversary model and apply it to document data. This work devises an attack algorithm for documents using a web search engine, and then proposes a privacy-preserving framework against the attacks. We evaluate the privacy risks for actual accident reports from schools and court documents. In experiments using these reports, we show that human-sanitized documents still contain privacy risks and that our proposed approach can contribute to risk reduction.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.778; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: A huge number of documents such as news articles, public reports, and personal essays have been released on websites and social media. Once documents containing privacy-sensitive information are published, the risk of privacy breaches increases, thus requiring very careful review of documents prior to publication. In many cases, human experts redact or sanitize documents before publishing them; however, this approach can be inefficient with regard to cost and accuracy. Furthermore, such measures do not guarantee that critical privacy risks are eliminated from the documents. In this paper, we present a generalized adversary model and apply it to document data. This work devises an attack algorithm for documents using a web search engine, and then proposes a privacy-preserving framework against the attacks. We evaluate the privacy risks for actual accident reports from schools and court documents. In experiments using these reports, we show that human-sanitized documents still contain privacy risks and that our proposed approach can contribute to risk reduction.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.778; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 62, 号 12, 発行日 2021-12-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764