PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
User Modelling for Computer Human Interaction
Theory & Algorithms
ID Code:2738
Deposited By:François Jacquenet
Deposited On:22 November 2006