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Author Jacquemont, S. ♦ Frangois Jacquenet, F. ♦ Sebban, M.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2006
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Automata ♦ Cities and towns ♦ Data privacy ♦ Web pages ♦ Counting circuits ♦ Cams ♦ Databases ♦ Unsolicited electronic mail ♦ Prototypes ♦ Humans
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
Description Author affiliation: Univ. Jean Monnet de Saint-Etienne (Jacquemont, S.; Frangois Jacquenet, F.; Sebban, M.)
ISBN 0769527280
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2006-11-13
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 338.26 kB
Page Count 8
Starting Page 347
Ending Page 354


Source: IEEE Xplore Digital Library