Hello, everyone, I'm Khalfoun Besma, a second year PhD student at INSA de Lyon in France.
I'm going to present how to enforce location privacy
through re-identification risk assessment with a federated learning approach.
This work is done in collaboration with my team Sonia ben mokhtar, Sara bouchenak and Vlad Nitu.
The increasing development of information and communication technologies with up to 75 billions
of connected devices in 2025 as forecast, and the growth of very fast networks like 5g have
contributed to the wide use of crowd sensing applications where geo located data is collected
for traffic monitoring, fitness monitoring, or air quality monitoring.
However, the disclosure of such mobility data may jeopardise user's privacy. For instance,
we can see two mobility traces in this picture, the green one and the red one. From this data,
we can infer sensitive information about users like point of interest, discover social links,
predict the next activity or location of the user, and finally,
disclose the real identity of the user. The latter is known as re-identification attacks.
How these re-identification attacks work ? First we suppose an attacker that has access to a set of
known data where the identity of users is revealed.
The attacker constructs mobility profiles, which provide a more
representative form of the past mobility. Next, when the attacker receives an anonymous
mobility trace, he constructs its anonymous mobility profile, compares it to the previously
built profiles and returns the user ID of the closest profile thanks to a similarity metric.
Here are some examples of mobility profiles. It can be a set of point of
interests like in poi-attack, a mobility Markov chain model like in pit-attack,
or a probability distribution of visited cells in the form of a heat map, like in ap-attack.
In order to mitigate these threats, many location privacy protection mechanisms
lppms have been proposed in the literature.
an lppm is defined as a function that takes as input one or multiple mobility records,
and produces an obfuscated version of the data. For that propose many techniques are developed.
Some of them rely on formal guarantees, such as differential privacy,
by adding Laplacian noise to GPS coordinates, or K-anonymity by hiding locations in at least
k minus one other users location. Finally, other techniques provide more practical
guarantees like promise, which erases POIs by forming equidistant points. However,
these lppms are applied blindly and released without any prior privacy risk assessment.
That's why more advanced systems proposed personalised protection by exploiting
existing or lppms and by including the privacy risk assessment before sharing the data.
For example, mood, which combines lppms off the shelf and evaluates the resulting data with
multiple re-identification attacks. GAN generates noise with a generator network and calibrates
its level thanks to a discriminator network based on user re-identification assessment.
And finally, alp adaptive location privacy, which finds the best privacy utility trade off
thanks to simulated annealing algorithm. The downside of these systems is that they require
a centralized knowledge of raw mobility data, which is still a single point of failure.
So the question that we are trying to handle is how can Alice achieve a better privacy utility
trade off with privacy risk assessment with centralizing raw data. Here comes
our contribution, EDEN a user side mobility data protection system for crowd sensing applications.
Let us consider Alice who wants to participate in a crowd sensing campaign using her smartphone.
Before sending the geo located data to mobile crowd sensing server MCS. Alice
locally sanitizes. Her mobility data thanks to a set of lppms with different configurations.
Then she evaluates each version of the resulting data with FURIA, a federated user
re-identification risk assessment model trained in a decentralised way on the user device.
If the test performed by FURIA passed, which means that you could not re-identify Alice
as the originating user of this trace. And if two lppms pass the risk assessment,
the one with the best Date utility in terms of area coverage of the raw data is sent to the MCS.
we evaluate eden on five real-world mobility data sets
we compared the privacy utility
tradeoff of individual lppms and
the privacy oracle on the geolife dataset
the privacy metric corresponds to the
resilience to a stronger re-identification
attacker combining state-of-the-art
attacks namely poi-attack, ap-attack and pit-attack
the utility metric corresponds to area
coverage of raw data.
The results show that EDEN provides a
better privacy utility tradeoff
to conclude our system can be improved
by extending it with new lppms and
utility metrics.
also we investigate how to secure
communications between the client side
and the master server side of furia with
secure multi-party aggregation
all implemented lppms attacks and
scripts to run experiments are available
on the following link
Thank you