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Measuring Personal Information Exposure in the Mobile and IoT Environments.- [electronic resource]
Measuring Personal Information Exposure in the Mobile and IoT Environments.- [electronic resource]
상세정보
- 자료유형
- 학위논문(국외)
- 자관 청구기호
- 기본표목-개인명
- 표제와 책임표시사항
- Measuring Personal Information Exposure in the Mobile and IoT Environments. - [electronic resource] / Ren, Jingjing.
- 발행, 배포, 간사 사항
- 발행, 배포, 간사 사항
- 형태사항
- 1 online resource(187 p.)
- 일반주기
- Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
- 일반주기
- Advisor: Choffnes, David.
- 학위논문주기
- Thesis (Ph.D.)--Northeastern University, 2019.
- 이용제한주기
- This item must not be sold to any third party vendors.
- 요약 등 주기
- 요약Mobile and Internet of Things (IoT) devices are increasingly present in our everyday lives. They are equipped with a wide array of rich sensors and offer ubiquitous connectivity. These properties make the devices perfect candidates for data harvesting and privacy invasion over the Internet. However, these devices are usually locked down by OSes and carriers, making it difficult for the research community-and average users-to understand and mitigate online privacy concerns like disclosure of information to third parties.I argue that improving online privacy requires building systems that identify and control private information transmission. Our key observation is that a privacy dissemination must (by definition) occur over the network, so it is possible to detect and mitigate the leakage in the network traffic. Using this approach, the first challenge is how to detect privacy dissemination in network traffic. To address the problem, I developed a system called ReCon to apply machine learning algorithms to reliably identify Personally Identifiable Information (PII) without knowing the PII values in advance. Second, to quantify and understand the online privacy, I conducted experiments in multiple platforms (mobile applications in Android, iOS, Windows and mobile browsers) and studied how privacy exposure from mobile apps evolved over the range of eight years. Third, I developed a holistic approach to detect media exposure from Android mobile apps. Lastly, I extend such analysis to IoT devices and developed techniques to quantify information exposure-even for encrypted traffic-with semi-automated experiments.The goal of my work is to enable any Internet user to understand and control the private information exposed by mobile and IoT devices over the Internet. My work has enabled substantial progress in the increasingly important area of online privacy, has been cited and used by regulators and investigative journalists, and provides opportunities to improve privacy for individual users and organizations.
- 주제명부출표목-일반주제명
- 부출표목-단체명
- 기본자료저록
- Dissertations Abstracts International. 81-04B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 원문정보보기
MARC
008200317s2019 ulk s 00 eng■001000015493544
■00520200217182139
■007cr
■020 ▼a9781687904973
■040 ▼d225006
■08204▼a004
■090 ▼a전자도서(박사논문)
■1001 ▼aRen, Jingjing.
■24510▼aMeasuring Personal Information Exposure in the Mobile and IoT Environments.▼h[electronic resource] ▼cRen, Jingjing.
■260 ▼a[S.l.]▼bNortheastern University. ▼c2019
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2019
■300 ▼a1 online resource(187 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 81-04, Section: B.
■500 ▼aAdvisor: Choffnes, David.
■5021 ▼aThesis (Ph.D.)--Northeastern University, 2019.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aMobile and Internet of Things (IoT) devices are increasingly present in our everyday lives. They are equipped with a wide array of rich sensors and offer ubiquitous connectivity. These properties make the devices perfect candidates for data harvesting and privacy invasion over the Internet. However, these devices are usually locked down by OSes and carriers, making it difficult for the research community-and average users-to understand and mitigate online privacy concerns like disclosure of information to third parties.I argue that improving online privacy requires building systems that identify and control private information transmission. Our key observation is that a privacy dissemination must (by definition) occur over the network, so it is possible to detect and mitigate the leakage in the network traffic. Using this approach, the first challenge is how to detect privacy dissemination in network traffic. To address the problem, I developed a system called ReCon to apply machine learning algorithms to reliably identify Personally Identifiable Information (PII) without knowing the PII values in advance. Second, to quantify and understand the online privacy, I conducted experiments in multiple platforms (mobile applications in Android, iOS, Windows and mobile browsers) and studied how privacy exposure from mobile apps evolved over the range of eight years. Third, I developed a holistic approach to detect media exposure from Android mobile apps. Lastly, I extend such analysis to IoT devices and developed techniques to quantify information exposure-even for encrypted traffic-with semi-automated experiments.The goal of my work is to enable any Internet user to understand and control the private information exposed by mobile and IoT devices over the Internet. My work has enabled substantial progress in the increasingly important area of online privacy, has been cited and used by regulators and investigative journalists, and provides opportunities to improve privacy for individual users and organizations.
■650 4▼aComputer science.
■71020▼aNortheastern University▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g81-04B.
■773 ▼tDissertation Abstract International
■791 ▼aPh.D.
■792 ▼a2019
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T15493544▼nKERIS
