Hierarchical Local Differential Privacy

• Item type: Journal(1)
• 公開日: 2023-12-15
• タイトル: Hierarchical Local Differential Privacy
• タイトル / 言語: en
• タイトル: Hierarchical Local Differential Privacy
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:次世代デジタルプラットフォームにおける情報流通を支えるセキュリティとトラスト] privacy enhancing technologies, PETs, local differential privacy
• 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 (ATR)／Presently with KDDI Research, Inc.
• 著者所属: Advanced Telecommunications Research Institute International (ATR)
• 著者所属: Advanced Telecommunications Research Institute International (ATR)
• 著者所属: KDDI Research, Inc.
• 著者所属: KDDI Research, Inc.
• 著者所属(英): en
• 著者所属(英): Advanced Telecommunications Research Institute International (ATR) / Presently with KDDI Research, Inc.
• 著者所属(英): en
• 著者所属(英): Advanced Telecommunications Research Institute International (ATR)
• 著者所属(英): en
• 著者所属(英): Advanced Telecommunications Research Institute International (ATR)
• 著者所属(英): en
• 著者所属(英): KDDI Research, Inc.
• 著者所属(英): en
• 著者所属(英): KDDI Research, Inc.
• 著者名: Tomoaki, Mimoto; Takashi, Matsunaka; Hiroyuki, Yokoyama; Toru, Nakamura; Takamasa, Isohara
• 著者名(英): Tomoaki, Mimoto; Takashi, Matsunaka; Hiroyuki, Yokoyama; Toru, Nakamura; Takamasa, Isohara
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The local differential privacy metric has attracted attention due to its quantitative nature, and many mechanisms have been studied for satisfying local differential privacy based on data formats and use cases. Local differential privacy mechanisms generally target a certain data space and perturb it sufficiently to provide indistinguishability of the data on that space. Therefore, individual data tends to be greatly disturbed so that even relatively simple tasks require a large amount of data to equalize the noise caused by the mechanism. In this paper, we define hierarchical local differential privacy, which is an extension of local differential privacy, and propose a mechanism to satisfy both local differential privacy and hierarchical local differential privacy. Hierarchical local differential privacy views a data space hierarchically as a set of smaller spaces, and instead of abandoning the privacy of data contained in different spaces, the amount of noise can be reduced. In this paper, we further design a hierarchical local differential privacy framework and achieve a privacy guarantee based on local differential privacy for all the data in the framework. Finally, we experimentally evaluate the proposed framework using image data. The framework allows control over the amount of information that can be disclosed, and furthermore, maintains a higher degree of utility than applying a simple local differential privacy mechanism.; ------------------------------; 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.31(2023) (online); DOI http://dx.doi.org/10.2197/ipsjjip.31.821; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: The local differential privacy metric has attracted attention due to its quantitative nature, and many mechanisms have been studied for satisfying local differential privacy based on data formats and use cases. Local differential privacy mechanisms generally target a certain data space and perturb it sufficiently to provide indistinguishability of the data on that space. Therefore, individual data tends to be greatly disturbed so that even relatively simple tasks require a large amount of data to equalize the noise caused by the mechanism. In this paper, we define hierarchical local differential privacy, which is an extension of local differential privacy, and propose a mechanism to satisfy both local differential privacy and hierarchical local differential privacy. Hierarchical local differential privacy views a data space hierarchically as a set of smaller spaces, and instead of abandoning the privacy of data contained in different spaces, the amount of noise can be reduced. In this paper, we further design a hierarchical local differential privacy framework and achieve a privacy guarantee based on local differential privacy for all the data in the framework. Finally, we experimentally evaluate the proposed framework using image data. The framework allows control over the amount of information that can be disclosed, and furthermore, maintains a higher degree of utility than applying a simple local differential privacy mechanism.; ------------------------------; 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.31(2023) (online); DOI http://dx.doi.org/10.2197/ipsjjip.31.821; ------------------------------
• 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 64, 号 12, 発行日 2023-12-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764
• 公開者 / 言語: ja
• 公開者 / 出版者: 情報処理学会