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Threats and opportunities of mobile sensing technology in personal privacy and public security

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TitleInfo
Title
Threats and opportunities of mobile sensing technology in personal privacy and public security
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Wang
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Chen
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1986-
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Chen Wang
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author
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Chen
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Yingying
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Yingying Chen
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Advisory Committee
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chair
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Roy
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Roy Yates
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Advisory Committee
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internal member
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Wei
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Sheng
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Sheng Wei
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Advisory Committee
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internal member
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Hong
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Hong Man
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Advisory Committee
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outside member
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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School of Graduate Studies
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Text
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theses
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2019
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2019-10
Language
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English
Abstract (type = abstract)
The proliferation of the mobile devices (e.g., smartphones, smartwatches and fitness trackers) has brought great convenience to our daily lives. Mobile users can enjoy the online access anytime and anywhere through WiFi or cellular services, monitor daily activities (e.g., walking steps) via wearable devices, or flexibly access the devices via touch screens and microphones. The pervasive mobile sensors can further benefit the public sector, such as providing real-time data for public transportation, emergency and public safety protection. While the mobile technologies facilitate a wide range of useful applications to the users, an adversary may leverage them to derive the user’s sensitive private information. This dissertation focuses on exploring the security threats of the mobile devices given the various embedded sensors. Moreover, we explore to utilize mobile sensing technologies as opportunities for protecting not only the personal privacy but also the public security.

As the smartphone is the most popular mobile device worldwide, we first investigate to what extent the users’ personal information such as social relationships and demographics could be revealed from their smartphones, in particular through the simple signal information of the pervasive Wi-Fi Access Points (AP) without examining any Wi-Fi traffic. We successfully derive the users’ activities at daily visited places from the surrounding APs and utilize that as the basis to infer the users’ social interactions and individual behaviors. Our approaches capture how closely people interact with each other based on their physical closeness to infer their social relationships and recognize the individual behaviors via their activity characteristics (e.g., activeness and time slots) at their daily visited places to estimate the users’ demographics.

Moreover, the increasing popularity of wearable devices motivates us to examine the possible sensitive information leakage from the user’s personal wearable devices. We demonstrate a serious security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people are accessing key-based security systems (e.g., ATM machines). We develop a system to show that the motion sensors on a wearable device can be exploited to discriminate mm-level distances and directions of the user’s fine-grained hand movements, which enables an adversary to reproduce the hand movement trajectories of the user to recover the secret key entries.

Besides security threats, we also find that mobile technologies bring unique opportunities to protect the personal privacy. We propose to use an off-the-shelf wearable device (e.g., a smartwatch or bracelet) as a secure token to secure the Voice Assistant (VA) systems (e.g., Google Home and Amazon Alexa), which have been shown to be under a high risk of sensitive information leakage in the various acoustic attacks (e.g., impersonation, replay and hidden command attacks). In particular, the proposed system exploits the motion sensors, readily available on most wearables, to describe the voice command in the vibration domain, which is then compared with the audio domain information (recorded by the VA device’s microphone) to verify whether the voice command comes from the legitimate user.

Finally, we provide a low-cost and easy-to-scale solution to address the ever-increasing public safety concerns caused by the portable dangerous objects (e.g., lethal weapons, chemical explosives and home-made bombs) in the public places such as museums, stadiums, theme parks and schools. Our proposed detection system utilizes the fine-grained channel state information (CSI) from existing WiFi networks to detect the existence of suspicious objects hidden inside baggage and further identify the dangerous material type of the object without penetrating the user’s privacy through physically opening the baggage. Compared to the existing X-ray based object scanning infrastructure, this detection system based on the commodity WiFi could become a game-changer, which significantly reduces the deployment cost and is easy to set up in numerous public venues.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (authority = local)
Topic
Mobile device
Subject (authority = LCSH)
Topic
Mobile computing
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Rutgers University Electronic Theses and Dissertations
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ETD_10133
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1 online resource (xv, 132 pages) : illustrations
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Ph.D.
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Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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doi:10.7282/t3-cmqh-5q77
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ETD doctoral
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Rights

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The author owns the copyright to this work.
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Name
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Wang
GivenName
Chen
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2019-07-17 14:34:54
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Chen Wang
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Rutgers University. School of Graduate Studies
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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