Item type |
学術雑誌論文 / Journal Article.(1) |
公開日 |
2019-08-26 |
書誌情報 |
en : IEEE Transactions on Information Forensics and Security
巻 14,
号 1,
p. 196-211,
発行日 2018-06-18
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タイトル |
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タイトル |
Location Privacy in Usage-Based Automotive Insurance: Attacks and Countermeasures |
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言語 |
en |
言語 |
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言語 |
eng |
キーワード |
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言語 |
en |
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主題Scheme |
Other |
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主題 |
Connected vehicles |
キーワード |
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言語 |
en |
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主題Scheme |
Other |
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主題 |
location privacy |
キーワード |
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言語 |
en |
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主題Scheme |
Other |
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主題 |
hidden Markov model |
キーワード |
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言語 |
en |
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主題Scheme |
Other |
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主題 |
secure aggregation protocol |
キーワード |
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言語 |
en |
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主題Scheme |
Other |
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主題 |
inspection game |
資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
アクセス権 |
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アクセス権 |
open access |
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アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
著者 |
ZHOU, Lu
DU, Suguo
ZHU, Haojin
CHEN, Cailian
太田, 香
董, 冕雄
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室蘭工業大学研究者データベースへのリンク |
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表示名 |
太田 香(OTA Kaoru) |
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URL |
http://rdsoran.muroran-it.ac.jp/html/100000140_ja.html |
室蘭工業大学研究者データベースへのリンク |
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表示名 |
董 冕雄(DONG Mianxiong) |
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URL |
http://rdsoran.muroran-it.ac.jp/html/100000145_ja.html |
抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Usage-based insurance (UBI) is regarded as a promising way to provide accurate automotive insurance rates by analyzing the driving behaviors (e.g., speed, mileage, and harsh braking/accelerating) of drivers. The best practice that has been adopted by many insurance programs to protect users' location privacy is the use of driving speed rather than GPS data. However, in this paper, we challenge this approach by presenting a novel speed-based location trajectory inference framework. The basic strategy of the proposed inference framework is motivated by the following observations. In practice, many environmental factors, such as real-time traffic and traffic regulations, can influence the driving speed. These factors provide side-channel information about the driving route, which can be exploited to infer the vehicle's trace. We implement our discovered attack on a public data set in New Jersey. The experimental results show that the attacker has a nearly 60% probability of obtaining the real route if he chooses the top 10 candidate routes. To thwart the proposed attack, we design a privacy preserving scoring and data audition framework that enhances drivers' control on location privacy without affecting the utility of UBI. Our defense framework can also detect users' dishonest behavior (e.g., modification of speed data) via a probabilistic audition scheme. Extensive experimental results validate the effectiveness of the defense framework. |
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言語 |
en |
出版者 |
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出版者 |
IEEE |
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言語 |
en |
出版者版へのリンク |
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表示名 |
10.1109/TIFS.2018.2848227 |
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URL |
https://doi.org/10.1109/TIFS.2018.2848227 |
DOI |
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関連タイプ |
isVersionOf |
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識別子タイプ |
DOI |
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関連識別子 |
10.1109/TIFS.2018.2848227 |
日本十進分類法 |
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主題Scheme |
NDC |
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主題 |
007 |
ISSN |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
1556-6013 |
書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA12122678 |
権利 |
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言語 |
en |
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権利情報 |
© 2018 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. |
著者版フラグ |
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出版タイプ |
AM |
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出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
フォーマット |
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内容記述タイプ |
Other |
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内容記述 |
application/pdf |