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Motion based side channels against m...

Yue, Qinggang.

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Motion based side channels against mobile devices.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Motion based side channels against mobile devices./
Author:	Yue, Qinggang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	146 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: A.
Contained By:	Dissertation Abstracts International78-11A(E).
Subject:	Philosophy of science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10643720
ISBN:	9780355165326

Motion based side channels against mobile devices.
Yue, Qinggang.

Motion based side channels against mobile devices. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 146 p.

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: A.

Thesis (Ph.D.)--University of Massachusetts Lowell, 2017.

Mobile devices are ubiquitously used and are mostly equipped with cameras. In this dissertation, we research a specific type of side channel formed by hand movement. which may leak private information such as passwords, text messages, emails, etc. We also discuss promising countermeasures to these attacks and develop the Privacy Enhancing Keyboard.

ISBN: 9780355165326Subjects--Topical Terms:

2079849
Philosophy of science.

Motion based side channels against mobile devices.
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: A.
500 $a Adviser: Xinwen Fu.
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520 $a Mobile devices are ubiquitously used and are mostly equipped with cameras. In this dissertation, we research a specific type of side channel formed by hand movement. which may leak private information such as passwords, text messages, emails, etc. We also discuss promising countermeasures to these attacks and develop the Privacy Enhancing Keyboard.
520 $a We first demonstrate how peoples hand movements reveal the touch inputs on touch-enabled devices while the attacker cannot see any texts or popups in a video of the victim tapping on the touch screen. The threat model is that a webcam, smartwatch, smartphone, or Google Glass is used for stealthy attacks in scenarios such as conferences and similar gathering places. Extensive experiments were performed to demonstrate the impact of this attack.
520 $a If the touch input is composed of meaningful texts such as email messages, we can further increase the attack success rate and range via natural language processing techniques because the text follows spelling and grammar rules and is context-sensitive. We model the process of retrieving the text from videos as noisy channels and apply the unigram and trigram language models for spelling correction. The NLP techniques greatly help increase the retrieving accuracy and the attack distance.
520 $a We further analyze how the trajectory of the hand movement helps the password retrieval while our preliminary work only considers each touch individually. In particular, we study how camera-equipped drones may use the trajectory based algorithms to breach user privacy and security. We first propose algorithms to derive and simplify the fingertip movement trajectory into a polygon or polyline, defined as target trajectory. Then, we derive the possible passcode candidates and their corresponding candidate trajectories. The candidate passcodes are ranked by the similarity between the corresponding candidate trajectory and the target trajectory. The candidate passcode with the highest similarity is selected as the most probable. Extensive experiments show the validity and severity of this attack.
520 $a The proposed attacks depend on the fixed layout of the keyboard. To defeat this kind of attack and related ones, we design and develop the Privacy Enhancing Keyboard (PEK). PEK will pop out a randomized software keyboard when it is leveraged to input sensitive information, such as passcodes. While, to keep its usability, a normal QWERTY keyboard will pop out if the user intends to input text messages or emails.
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