WEKO3
アイテム
Oblivious Statistic Collection With Local Differential Privacy in Mutual Distrust
http://hdl.handle.net/10061/0002000493
http://hdl.handle.net/10061/00020004931b25d12a-0189-4e37-82b9-f037ee53c02c
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2024-07-02 | |||||
| タイトル | ||||||
| タイトル | Oblivious Statistic Collection With Local Differential Privacy in Mutual Distrust | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Local differential privacy | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | oblivious transfer protocol | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | location data | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | privacy-preserving data mining | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | data security | |||||
| 資源タイプ | ||||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | open access | |||||
| 著者 |
笹田, 大翔
× 笹田, 大翔× 妙中, 雄三× 門林, 雄基 |
|||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Location data is valuable for various applications such as epidemiology, natural disasters, and urban planning but causes exposure of sensitive information, e.g., home or work place, from collected data in a datastore. Local Differential Privacy (LDP)-based data collection is a promising technology to protect sensitive information. A mobile device modify data to make each piece of data indistinguishable from others but keep its intrinsic value for statistical characteristics in data. Although LDP fundamentally protects the privacy exposure from a data store, a datastore suffer a shortcomings on it; as a datastore can never validate the modified data due to concealed raw data, that allows anyone to tamper with one’s data or inject any amount of data, and thus manipulate the statistics of the whole data in a datastore, called data poisoning attack. As a device does not disclose raw data and a datastore cannot collaborate to validate data with a device who may be an adversary on this mutual distrust relationship, data collection needs an ability to avoid the effect of data poisoning.. The cause of data poisoning is the direct relationship between data volume and statistic; the more data a device sends gives more statistical changes on merged data in a datastore. In this paper, we propose to decouple statistical characteristics from data volumes on LDP-based data collection process to minimize the effect of poisoned data on a datastore. We utilize Oblivious Transfer (OT) protocol to retrieve only statistic characteristics of receiving data at a datastore. As OT protocol inevitably strengthen privacy protection on LDP-based data collection and accordingly drops statistic characteristics of data, We adjust LDP processing to collaboratively work with OT protocol. The proposed adjustment method adapts the protection strength of LDP to OT protocol behavior so that a data store receives data containing sufficient statistical characteristics. We conduct qualitative and experimental overhead analysis and show that our method decouples the relationship between statistical characteristics from data volume. Our experimental result also prove that the overhead can be acceptable on devices such as smartphones and IoT. | |||||
| 書誌情報 |
en : IEEE Access 巻 11, p. 21374-21386, 発行日 2023-03-02 |
|||||
| 出版者 | ||||||
| 出版者 | IEEE | |||||
| ISSN | ||||||
| 収録物識別子タイプ | EISSN | |||||
| 収録物識別子 | 2169-3536 | |||||
| 出版者版DOI | ||||||
| 関連タイプ | isReplacedBy | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1109/ACCESS.2023.3251560 | |||||
| 出版者版URI | ||||||
| 関連タイプ | isReplacedBy | |||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://ieeexplore.ieee.org/abstract/document/10057407 | |||||
| 権利 | ||||||
| 権利情報Resource | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||
| 権利情報 | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||
| 著者版フラグ | ||||||
| 出版タイプ | NA | |||||
