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Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing
https://uec.repo.nii.ac.jp/records/8924
https://uec.repo.nii.ac.jp/records/892405393b39-0d1b-4a24-a268-1f25eeb7d8de
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
3-IFS (2.9 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2019-01-22 | |||||
| タイトル | ||||||
| タイトル | Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Mobile crowdsensing | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | privacy | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | data mining | |||||
| 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 |
Sei, Yuichi
× Sei, Yuichi× Ohsuga, Akihiko |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Mobile crowdsensing, which collects environmental information from mobile phone users, is growing in popularity. These data can be used by companies for marketing surveys or decision making. However, collecting sensing data from other users may violate their privacy. Moreover, the data aggregator and/or the participants of crowdsensing may be untrusted entities. Recent studies have proposed randomized response schemes for anonymized data collection. This kind of data collection can analyze the sensing data of users statistically without precise information about other users' sensing results. However, traditional randomized response schemes and their extensions require a large number of samples to achieve proper estimation. In this paper, we propose a new anonymized data-collection scheme that can estimate data distributions more accurately. Using simulations with synthetic and real datasets, we prove that our proposed method can reduce the mean squared error and the JS divergence by more than 85% as compared with other existing studies. | |||||
| 書誌情報 |
en : IEEE Transactions on Information Forensics and Security 巻 12, 号 4, p. 926-939, 発行日 2017-04 |
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| 出版者 | ||||||
| 出版者 | IEEE | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1556-6013 | |||||
| DOI | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1109/TIFS.2016.2632069 | |||||
| 権利 | ||||||
| 権利情報 | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||
| 関連サイト | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1109/TIFS.2016.2632069 | |||||
| 著者版フラグ | ||||||
| 出版タイプ | AM | |||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
