Inferring Touch from Motion in Real World Data

Bissig, Pascal; Brandes, Philipp; Passerini, Jonas; Wattenhofer, Roger

doi:10.1007/978-3-319-30303-1_4

Cited by 1 publication

(1 citation statement)

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“…In terms of security, the motion sensors are not considered as exposing sensitive information, so the popular mobile operating systems (iOS, Android) allow all installed applications to access the sensor data. However, recent studies, like [1], [2], [3], [4], [5], [6], [7] have demonstrated that user security can be compromised by employing device motion sensor data as a side-channel. Specifically, the presented attacks infer user taps on smartphone touchscreens by processing motion sensor readings, i.e user taps on the soft keyboards of smartphones can be guessed.…”

Section: Introductionmentioning

confidence: 99%

Age group detection using smartphone motion sensors

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et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Side-channel attacks revealing the sensitive user data through the motion sensors (such as accelerometer, gyroscope, and orientation sensors) emerged as a new trend in the smartphone security. In this respect, recent studies have examined feasibility of inferring user's tap input by utilizing the motion sensor readings and propounded that some user secrets can be deduced by adopting the different side-channel attacks. More precisely, in this kind of attacks, a malware processes outputs of these sensors to exfiltrate victims private information such as PINs, passwords or unlock patterns. In this paper, we describe a new side-channel attack on smartphones that aims to predict the age interval of the user. Unlike the previous works, our attack does not directly deal with recovering a target user's some secret, rather its sole purpose is determining whether she is a child or an adult. The main idea behind our study relies on the key observation that the characteristics of children and adults differ in hand holding and touching the smartphones. Consequently, we show that there is an apparent correlation between the motion sensor readings and these characteristics that build up our attack strategy. In order to exhibit efficiency of the proposed attack, we have developed an Android application named as BalloonLogger that evaluates accelerometer sensor data and perform child/adult detection with a success rate of 92.5%. To the best of our knowledge, in this work, for the first time, we point out such a security breach.

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Section: Introductionmentioning

confidence: 99%