Researchers found that accelerometer data from smartphones can reveal people's location, passwords, body features, age, gender, level of intoxication, driving style, and be used to reconstruct words s

detection of (Matic 2013)
speech activity (Englebienne 2012) Interactions
(Matic 2013)
(Pesonen 2018,
reconstruction of van Hees 2015, sleep patterns (Alvarez 2007)
spoken words Bhagat 2014, and quality health condition
Borghese 2018) (zeit 2018)
i eitzer
detection of (Zhang 2015) (Davarci 2017)
sound vibrations
(Jain 2016,
jender
Weiss etal
(Artese 2017, level of (Artese 2017
rsonaility trai
Wilson 2015, physical activity Wilson 2015) personaly
Crouter 2003)
body movements Owusu 2012)
of the user (Gruenenfelder-Steiger
weight of carried 2017)
(Williamson 2015) objects
moods and
(Zhang 2016, emotions
accelerometer data Garcia-Ceja 2016,
from mobile devices (Gupta 2014, Olsen 2016)
Chernbumroong 2011, activity recognition
Mannini 2013)
(Saleheen 2015, smoking behavior
motion trajectory Tang 2014) 9
of the device
(Zhang 2009, eating drinking
Thomaz 2015) moments (Kwapisz 2010,
Srilekha 2015,
Nickel 2012,
Dai 2010) 2016, user identification
Dey 2014) (Hua 2015, (Weiss 2011, body height Primo 2014)
Han 2012) Yanai 2016)
(Weiss 2011)
body weight
device fingerprint (Arnold 2015,
Gharani 2017,
ivi i Killian 2018
level of intoxication
(Dai 2010)
geographic (Freudiger 2011,
location Golle 2009)
(Das 2016,
Dey 2014)
Source: Kréger (2019) “Privacy Implications of Accelerometer Data: A Review of Possible Inferences”
In: Proceedings of the International Conference on Cryptography, Security and Privacy (ICCSP ’19). ACM, New York