I am going to talk about our work entitled "Where you go Matters: A study on the privacy
implications of continuous location tracking"
I am Mirco Musolesi and the co-author of this work is Benjamin Baron. Benjamin was a postdoctoral
fellow in my lab. He is now in industry at Apple.
This work is about location and privacy. In fact, an increasing number of apps and services
are continuously tracking the locations of a large number of users.
This happens directly or indirectly, through the apps themselves or, for example through
ad frameworks
Location data can reveal personal information, which can be useful for third parties such
as advertisers.
In fact a person is tracked during the day and information is collected about their mobility
patterns and significant places can be detected from them.
By analysing these traces, a third party entity can infer information about the location of
the home (and indirectly characteristics about income and social status etc.), ethnicity,
religious or political beliefs, occupation, personal interests and so on.
Indeed, very refined models have been developed and have been developing for the extraction
of this personal information from places for a variety of goals.
The key research question of our paper can be summarised as follows: What are the privacy
expectations about the personal information that can be inferred from continuously collected
location data?
In order to answer this question, we develop an "in-the-wild" research study.
We first develop a mobile application for data collection, asking feedback from the
user through questionnaires.
We then extracted personal information by means of an extraction pipeline.
Finally, we analysed the data in order to derive insights for researchers and practitioners
The application that we developed was called TrackingAdvisor.
The application implements mechanisms for energy efficient background location tracking.
It also allows for efficiently collecting feedback from participants.
TrackingAdvisor consists of a mobile application that was developed for iOS and a backend server.
The application asks for the permission to always track the location of the user and
collects raw location traces in the background.
When receiving the location traces, the backend starts a pipeline that in turn extracts stay
points from the trace.
By doing so we derive the location (i.e., point of interest), where the user stayed
at, for some time.
Then we match these locations to known places from a place database, Foursquare in our case.
The matched place is then shown to the user so that they can confirm, delete, or correct
it.
From the place, the pipeline infers a set of personal information that is then shown
to the user so that they can confirm whether the personal information is relevant to the
place or not.
Finally, the application periodically asks the user to rate their privacy sensitivity
w.r.t. the personal information extracted by the system.
We are going now to present an overview of some key results. We would like to refer to
the papers for an extensive analysis of our experimental findings.
In this plot we observe that topics of interests, ethnicity and socio-economic status are considered
very sensitive.
We note that there is a big variation in terms of sensitivity for the different users.
This motivates the use of personalised privacy preferences
To summarise.
We presented a methodology to conduct an in-the-wild study with a mobile app, called TrackingAdvisor
to analyse the personal information we can infer from location data and to capture the
privacy preferences of the participants.
Participants perceived categories of personal information as privacy-sensitive with varying
degrees of privacy sensitivity, also across the participants themselves.
Interpretability of fine-grained privacy sensitivities are key to making relevant recommendations
that help users to take useful actions to protect themselves against unwanted personal
information inferences.
Thanks for the attention.