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Mobile Data Transparency and Control
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Mobile Data Transparency and Control. |
| 개인저자 | Shuba, Anastasia. |
| 단체저자명 | University of California, Irvine. Electrical and Computer Engineering - Ph.D.. |
| 발행사항 | [S.l.]: University of California, Irvine., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 141 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085608824 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Irvine, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Markopoulou, Athina. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Mobile devices carry sensitive information that is routinely transmitted over the network and is often collected for analytics and targeted advertising purposes. Users are typically unaware of this data collection and have little control over their information. In this thesis, we develop systems and techniques that provide users with increased transparency and control over their mobile data. We develop AntMonitor-a user space app that can inspect and analyze all network traffic coming in and out of the device. AntMonitor outperforms prior art in terms of network throughput and CPU usage. Our tool enables real-time packet interception and analysis on the mobile device, including the following three applications. First, we build AntShield, which builds on top of AntMonitor to intercept packets, and uses a combination of deep packet inspection and machine learning to detect and block outgoing traffic that contains personally identifiable information. Second, we develop NoMoAds-the first mobile-specific, cross-app, machine learning-based ad-blocker, and we show that it outperforms state-of-the-art filter lists. Third, we build AutoLabel-a system for automatically labeling which packets were sent by apps and which by third-party advertisement or analytics libraries. This eliminates the need for manual labeling-which is the major bottleneck in creating filter lists and classifiers. Overall, this thesis follows a network-based approach to enhance mobile data transparency and give users control over their data. |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
