ACCessory: password inference using accelerometers on smartphones

By Emmanuel Owusu, Jun Han, Sauvik Das, Adrian Perrig, Joy Ying Zhang. Workshop on Mobile Computing Systems and Applications, 2012

423 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/190c25f9f564b4703dc362b21c7fe3996a098ef4

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Citations in the corpus

Continuous Authentication for Voice Assistants (2017, background): “Researchers have studied the potential applications of accelerometers for human behavior analysis [3, 24, 34, 42].”
Mobile sensor data anonymization (2018, methodology): “ser identity for user authentication [22]. However, other untrusted applications with direct access to raw sensory data may infer private information (e.g. inferring passwords from accelerometer data [24]). We are therefore interested in designing a privacy-preserving method that gives apps an ondev”
Spearphone: A Speech Privacy Exploit via Accelerometer-Sensed Reverberations from Smartphone Loudspeakers (2019, methodology): “This could cause sensitive information leakage on mobile devices [9], [3], [10], [5], [11].”
WALNUT: Waging Doubt on the Integrity of MEMS Accelerometers with Acoustic Injection Attacks (2017, methodology): “Similarly, Owusu and Aviv show smart-phone accelerometer information leakage can be leveraged to infer user touch-screen gestures and key presses to leak passwords and PIN codes to unlock phones [14], [15].”
DolphinAttack: Inaudible Voice Commands (2017, methodology): “[40] utilized the accelerometer readings as a side channel to extract the entire sequences of entered text on a smartphone touchscreen keyboard without requiring special privileges.”
AccelPrint: Imperfections of Accelerometers Make Smartphones Trackable (2014, background): “, TouchLogger [15], ACCessory [49], Taplogger [67]) have been built.”
Tapprints: your finger taps have fingerprints (2012, background): “[7] present, respectively, TouchLogger and ACCessory, both relying on the accelerometer for key press inference on soft-keyboards.”
Intel SGX Explained (2016, methodology): “[148] applied similar techniques to learn a user’s input on a smartphone’s on-screen keyboard, based on data from the device’s accelerometer.”
You Are How You Touch: User Verification on Smartphones via Tapping Behaviors (2014, background): “However, given the same mobile device, behavioral biometrics tend to be less sensitive to the surrounding environmental factors like darkness or noise.”