Tejas Kannan (University of Chicago), Synthia Qia Wang (University of Chicago), Max Sunog (University of Chicago), Abraham Bueno de Mesquita (University of Chicago Laboratory Schools), Nick Feamster (University of Chicago), Henry Hoffmann (University of Chicago)

Smart Televisions (TVs) are internet-connected TVs that support video streaming applications and web browsers. Users enter information into Smart TVs through on-screen virtual keyboards. These keyboards require users to navigate between keys with directional commands from a remote controller. Given the extensive functionality of Smart TVs, users type sensitive information (e.g., passwords) into these devices, making keystroke privacy necessary. This work develops and demonstrates a new side-channel attack that exposes keystrokes from the audio of two popular Smart TVs: Apple and Samsung. This side-channel attack exploits how Smart TVs make different sounds when selecting a key, moving the cursor, and deleting a character. These properties allow an attacker to extract the number of cursor movements between selections from the TV's audio. Our attack uses this extracted information to identify the likeliest typed strings. Against realistic users, the attack finds up to 33.33% of credit card details and 60.19% of common passwords within 100 guesses. This vulnerability has been acknowledged by Samsung and highlights how Smart TVs must better protect sensitive data.

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FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting

Meenatchi Sundaram Muthu Selva Annamalai (University College London), Igor Bilogrevic (Google), Emiliano De Cristofaro (University of California, Riverside)

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SigmaDiff: Semantics-Aware Deep Graph Matching for Pseudocode Diffing

Lian Gao (University of California Riverside), Yu Qu (University of California Riverside), Sheng Yu (University of California, Riverside & Deepbits Technology Inc.), Yue Duan (Singapore Management University), Heng Yin (University of California, Riverside & Deepbits Technology Inc.)

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EyeSeeIdentity: Exploring Natural Gaze Behaviour for Implicit User Identification...

L Yasmeen Abdrabou (Lancaster University), Mariam Hassib (Fortiss Research Institute of the Free State of Bavaria), Shuqin Hu (LMU Munich), Ken Pfeuffer (Aarhus University), Mohamed Khamis (University of Glasgow), Andreas Bulling (University of Stuttgart), Florian Alt (University of the Bundeswehr Munich)

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