Privacy Disclosure Adaptation for Trading between Personal Attributes and Incentives

• Item type: Journal(1)
• 公開日: 2016-12-15
• タイトル: Privacy Disclosure Adaptation for Trading between Personal Attributes and Incentives
• タイトル / 言語: en
• タイトル: Privacy Disclosure Adaptation for Trading between Personal Attributes and Incentives
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：ヒトとモノを中心としたセキュアで信頼できる社会に向けて] privacy, privacy disclosure, privacy trading
• 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: SOKENDAI (The Graduate University of Advanced Studies)
• 著者所属: The University of Tokyo
• 著者所属: National Institute of Informatics
• 著者所属(英): en
• 著者所属(英): SOKENDAI (The Graduate University of Advanced Studies)
• 著者所属(英): en
• 著者所属(英): The University of Tokyo
• 著者所属(英): en
• 著者所属(英): National Institute of Informatics
• 著者名: Ake, Osothongs; Vorapong, Suppakitpaisarn; Noboru, Sonehara
• 著者名(英): Ake, Osothongs; Vorapong, Suppakitpaisarn; Noboru, Sonehara
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Products and services nowadays need personal information from consumers in order to personalize their goods to best fit consumers. At the present, the online environment is the biggest source of consumers' personal information. However, online privacy has become the major concern of consumers. A personal information trading platform has been proposed as a medium for collecting consumers' personal information in exchange for monetary incentive. This study proposes a new approach to requesting personal attributes which can adapt with consumers' personal information disclosure behavior and aims to increase the disclosure of personal information without increasing of monetary incentive. To develop this new adaption method, we developed the valuation of a personal information method without using currency. The probability and graph mining techniques were used to valuating personal attributes. Then, we displayed the relationships of personal attributes disclosure in the hierarchy and proposed a method for valuating personal information disclosure. The valuation method was used in the evaluations, which were compared with the disclosure of personal information results from the consumers. After the evaluation was completed, the result showed that the new approach can significantly increase the disclosure of consumers' personal information.; ------------------------------; 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.25(2017)(online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Products and services nowadays need personal information from consumers in order to personalize their goods to best fit consumers. At the present, the online environment is the biggest source of consumers' personal information. However, online privacy has become the major concern of consumers. A personal information trading platform has been proposed as a medium for collecting consumers' personal information in exchange for monetary incentive. This study proposes a new approach to requesting personal attributes which can adapt with consumers' personal information disclosure behavior and aims to increase the disclosure of personal information without increasing of monetary incentive. To develop this new adaption method, we developed the valuation of a personal information method without using currency. The probability and graph mining techniques were used to valuating personal attributes. Then, we displayed the relationships of personal attributes disclosure in the hierarchy and proposed a method for valuating personal information disclosure. The valuation method was used in the evaluations, which were compared with the disclosure of personal information results from the consumers. After the evaluation was completed, the result showed that the new approach can significantly increase the disclosure of consumers' personal information.; ------------------------------; 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.25(2017)(online); ------------------------------
• 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 57, 号 12, 発行日 2016-12-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764