How picking up your smartphone could reveal your identity

Date:
February 17, 2022
Source:
Lancaster University
Summary:
The time a person spends on different smartphone apps is enough to
identify them from a larger group in more than one in three cases
say researchers, who warn of the implications for security and
privacy. They fed 4,680 days of app usage data into statistical
models. Each of these days was paired with one of the 780 users,
such that the models learnt people's daily app use patterns. The
researchers then tested whether models could identify an individual
when provided with only a single day of smartphone activity that
was anonymous and not yet paired with a user.

Software granted access to a smartphone's standard activity logging
could render a reasonable prediction about a user's identity even
when they were logged out of their account. An identification is
possible with no monitoring of conversations or behaviors within
apps themselves.



FULL STORY ==========================================================================
The time a person spends on different smartphone apps is enough to
identify them from a larger group in more than one in three cases say researchers, who warn of the implications for security and privacy.


========================================================================== Psychologists Dr Heather Shaw, Professor Paul Taylor and Professor Stacey Conchie from Lancaster University, and Dr David Ellis from the University
of Bath analysed smartphone data from 780 people.

Their paper is published in the journal Psychological Science.

They fed 4,680 days of app usage data into statistical models. Each of
these days was paired with one of the 780 users, such that the models
learnt people's daily app use patterns.

The researchers then tested whether models could identify an individual
when provided with only a single day of smartphone activity that was
anonymous and not yet paired with a user.

Dr Ellis from the University of Bath said: "Our models, which were trained
on only six days of app usage data per person, could identify the correct person from a day of anonymous data one third of the time." That might
not sound like much, but when the models made a prediction regarding
who data belonged to, it could also provide a list of the most to the
least likely candidates. It was possible to view the top 10 most likely individuals that a specific day of data belonged to. Around 75% of the
time, the correct user would be among the top 10 most likely candidates.

Professor Taylor from Lancaster University added: "In practical terms, a
law enforcement investigation seeking to identify a criminal's new phone
from knowledge of their historic phone use could reduce a candidate pool
of approximately 1,000 phones to 10 phones, with a 25% risk of missing
them." Consequently, the researchers warn that software granted access
to a smartphone's standard activity logging could render a reasonable prediction about a user's identity even when they were logged-out of
their account. An identification is possible with no monitoring of conversations or behaviours within apps themselves.

Dr Shaw from Lancaster University said: "We found that people exhibited consistent patterns in their application usage behaviours on a day-to-day basis, such as using Facebook the most and the calculator app the
least. In support of this, we also showed that two days of smartphone data
from the same person exhibited greater similarity in app usage patterns
than two days of data from different people." Therefore, it is important
to acknowledge that app usage data alone, which is often collected by
a smartphone automatically, can potentially reveal a person's identity.

While providing new opportunities for law enforcement, it also poses
risks to privacy if this type of data is misused.

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========================================================================== Journal Reference:
1. Heather Shaw, Paul J. Taylor, David A. Ellis, Stacey M. Conchie.

Behavioral Consistency in the Digital Age. Psychological Science,
2022; 095679762110404 DOI: 10.1177/09567976211040491 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220217090711.htm

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