Sequence Mining Without Sequences: A New Way for Privacy Preserving

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