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Jingchao Sun

Researcher at Arizona State University

Publications -  28
Citations -  874

Jingchao Sun is an academic researcher from Arizona State University. The author has contributed to research in topics: Mobile device & Authentication. The author has an hindex of 18, co-authored 27 publications receiving 736 citations. Previous affiliations of Jingchao Sun include Yahoo! & Huazhong University of Science and Technology.

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TouchIn: Sightless Two-factor Authentication on Multi-touch Mobile Devices

TL;DR: This paper focuses on designing, implementing, and evaluating TouchIn, a two-factor authentication system on multi-touch mobile devices that allows the user to draw on arbitrary regions on the touchscreen without looking at it.
Proceedings ArticleDOI

Your song your way: Rhythm-based two-factor authentication for multi-touch mobile devices

TL;DR: RhyAuth is a novel two-factor rhythm-based authentication scheme that depends on a user-chosen rhythm and also the behavioral metrics for inputting the rhythm that is highly secure against various attacks and also very usable for both sighted and visually impaired people.
Proceedings ArticleDOI

VISIBLE: Video-Assisted Keystroke Inference from Tablet Backside Motion.

TL;DR: VISIBLE is a novel video-assisted keystroke inference framework to infer a tablet user’s typed inputs from surreptitious video recordings of tablet backside motion, built upon the observation that the keystrokes on different positions of the tablet”s soft keyboard cause its backside to exhibit different motion patterns.
Proceedings ArticleDOI

TouchIn: Sightless Two-factor Authentication on Multi-touch Mobile Devices

TL;DR: TouchIn as mentioned in this paper is a two-factor authentication system for multi-touch mobile devices that allows users to draw on arbitrary regions on the touchscreen without looking at it, which makes it very easy to use and also robust to shoulder-surfing and smudge attacks.
Proceedings ArticleDOI

Privacy-Preserving Social Media Data Outsourcing

TL;DR: A framework for differentially privacy-preserving social media data outsourcing for the first time in literature is proposed, based on a novel notion of E - text indistinguishability, which is proposed to thwart the text-based user-linkage attack.