TY - JOUR

T1 - Temporal and cultural limits of privacy in smartphone app usage

AU - Sekara, Vedran

AU - Alessandretti, Laura

AU - Mones, Enys

AU - Jonsson, Håkan

N1 - Publisher Copyright: © 2021, The Author(s).

PY - 2021

Y1 - 2021

N2 - Large-scale collection of human behavioural data by companies raises serious privacy concerns. We show that behaviour captured in the form of application usage data collected from smartphones is highly unique even in large datasets encompassing millions of individuals. This makes behaviour-based re-identification of users across datasets possible. We study 12 months of data from 3.5 million people from 33 countries and show that although four apps are enough to uniquely re-identify 91.2% of individuals using a simple strategy based on public information, there are considerable seasonal and cultural variations in re-identification rates. We find that people have more unique app-fingerprints during summer months making it easier to re-identify them. Further, we find significant variations in uniqueness across countries, and reveal that American users are the easiest to re-identify, while Finns have the least unique app-fingerprints. We show that differences across countries can largely be explained by two characteristics of the country specific app-ecosystems: the popularity distribution and the size of app-fingerprints. Our work highlights problems with current policies intended to protect user privacy and emphasizes that policies cannot directly be ported between countries. We anticipate this will nuance the discussion around re-identifiability in digital datasets and improve digital privacy.

AB - Large-scale collection of human behavioural data by companies raises serious privacy concerns. We show that behaviour captured in the form of application usage data collected from smartphones is highly unique even in large datasets encompassing millions of individuals. This makes behaviour-based re-identification of users across datasets possible. We study 12 months of data from 3.5 million people from 33 countries and show that although four apps are enough to uniquely re-identify 91.2% of individuals using a simple strategy based on public information, there are considerable seasonal and cultural variations in re-identification rates. We find that people have more unique app-fingerprints during summer months making it easier to re-identify them. Further, we find significant variations in uniqueness across countries, and reveal that American users are the easiest to re-identify, while Finns have the least unique app-fingerprints. We show that differences across countries can largely be explained by two characteristics of the country specific app-ecosystems: the popularity distribution and the size of app-fingerprints. Our work highlights problems with current policies intended to protect user privacy and emphasizes that policies cannot directly be ported between countries. We anticipate this will nuance the discussion around re-identifiability in digital datasets and improve digital privacy.

U2 - 10.1038/s41598-021-82294-1

DO - 10.1038/s41598-021-82294-1

M3 - Journal article

C2 - 33594096

AN - SCOPUS:85100941364

VL - 11

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 3861

ER -