Sequence Mining Without Sequences: a New Way for Privacy Preserving
Stéphanie Jacquemont, François Jacquenet and Marc Sebban
In: International Conference on Tools with Artificial Intelligence, 13-15 Nov 2006, Washington, USA.
During the last decade, sequential pattern mining has been the core of
numerous researches. It is now possible to efficiently discover users'
behavior in various domains such as purchases in supermarkets, Web site
visits, etc. Nevertheless, classical algorithms do not respect
individual's privacy, exploiting personal information (name, IP address, etc.).
We provide an original solution to privacy preserving by using a probabilistic automaton instead
of the original data.
An application in car flow modeling is presented, showing the
ability of our algorithm to discover frequent routes without any
individual information. A comparison with SPAM is
done showing that even if we sample from the automaton, our
approach is more efficient.