Sequence Mining Without Sequences: a New Way for Privacy Preserving

Stéphanie Jacquemont (1) , François Jacquenet (1) , Marc Sebban (1)

1 LHC - Laboratoire Hubert Curien

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 modelization 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.

Mots clés: Sequence Mining; Grammatical Inference

Domaines: Apprentissage [cs.LG]

Stéphanie Jacquemont, François Jacquenet, Marc Sebban. Sequence Mining Without Sequences: a New Way for Privacy Preserving. 2006, pp.347-354. ⟨hal-00114132⟩