Mohd Sabra (University of Texas at San Antonio), Anindya Maiti (University of Oklahoma), Murtuza Jadliwala (University of Texas at San Antonio)

Due to recent world events, video calls have become the new norm for both personal and professional remote communication. However, if a participant in a video call is not careful, he/she can reveal his/her private information to others in the call. In this paper, we design and evaluate an attack framework to infer one type of such private information from the video stream of a call -- keystrokes, i.e., text typed during the call. We evaluate our video-based keystroke inference framework using different experimental settings, such as different webcams, video resolutions, keyboards, clothing, and backgrounds. Our high keystroke inference accuracies under commonly occurring experimental settings highlight the need for awareness and countermeasures against such attacks. Consequently, we also propose and evaluate effective mitigation techniques that can automatically protect users when they type during a video call.

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Low-risk Privacy-preserving Electric Vehicle Charging with Payments

Andreas Unterweger, Fabian Knirsch, Clemens Brunner and Dominik Engel (Center for Secure Energy Informatics, Salzburg University of Applied Sciences, Puch bei Hallein, Austria)

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Google/Apple Exposure Notification Due Diligence

Douglas Leith and Stephen Farrell (Trinity College Dublin)

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GALA: Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural...

Qiao Zhang (Old Dominion University), Chunsheng Xin (Old Dominion University), Hongyi Wu (Old Dominion University)

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Improving Signal's Sealed Sender

Ian Martiny (University of Colorado Boulder), Gabriel Kaptchuk (Boston University), Adam Aviv (The George Washington University), Dan Roche (U.S. Naval Avademy), Eric Wustrow (University of Colorado Boulder)

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