I’m currently looking more into security aspects of distributed systems, with most efforts going into privacy preservation when monitoring crowds.
Monitoring crowds
Since a number of years I have been concentrating on monitoring the mobility of people, assuming they carry a device such as an electronic badge or a smartphone.
Our current research mainly concentrates on increasing the accuracy of detections (and thus the data analyses) and preserving privacy, which form part of our Living Smart Campus project at the UT. Much of this work is done in collaboration with the Polytechnical University of Bucharest.
WiFi scanning: what we do (protecting privacy)
The most important component of our current work is to monitor crowd flows while preserving privacy by design. This has turned out to be a nasty problem that has not been addressed enough. Counting the number of devices at a single location while protecting them from being identified is relatively simple. The dificulty is counting the number of devices that move from location A to location B in a privacy-preserving manner. We are exploring two types of solutions.
The first is to digitally hide a device in a crowd of devices, known as detection k-anonymity. In essence, we assign the same identifier to multiple devices. Easy for a single location, but not that easy if you want to cover movements between between any two arbitrarily chosen sensors at different locations.
The second is to have sensors save detections in encrypted Bloom filters. Bloom filters are bit strings used to represent sets and essentially support only membership testing, basic set operations (intersection, union) and estimating the size of a set. These properties allow us to compute the Bloom filter representing the intersection of what we detected at A and what was later detected at B, and thus the size of that set, which is the number of devices that had moved from A to B. Proper encryption and shuffling the bits prevents that neither the sensors, nor even the entity capable of decrypting a Bloom filter to discover detections. Only statistical counts can be retrieved from this setup.
WiFi scanning: what we did (understanding measurements)
Much of the current efforts are targeted toward more practical crowd monitoring, namely through scanning of WiFi-enabled personal devices such as smartphones. There are important differences with using badges. First, because so many people carry a smartphone, large-scale experiments with thousands of devices become possible. We have monitored multi-day festivals with over 100,000 participants. Second, WiFi data is extremely noisy, meaning that there is a tremendous data-analytics problem before we can even draw conclusions. Third, because smartphones do not detect each other, we have essentially lost a very powerful instrument: our proximity graphs. Fourth, because we are unintrusively monitoring personal devices, there are serious privacy issues to deal with.
We have run experiments with indoor and outdoor (see picture) sensors, previously provided by BlueMark Innovations.
Back in the old days: using active badges
Our first efforts toward crowd monitoring used active badges. Participants were required to use a home-brewed device that deployed a wireless so-called gossiping protocol to exchange information. The main challenge was to devise a large-scale wireless system in which the badges operated on a very low duty cycle (less than 1%, meaning they were passively asleep 99% of the time), while waking up all at the same time. During the active period they would be able to detect each other, which was the information we used to extract a proximity graph. This is a spatial-temporal graph reflecting which devices had detected each other (see the example). We managed to build real systems with over 400 badges and ran simulations demonstrating we could handle thousands of devices that stayed synchronized even in the presence of network partitions.
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