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Privacy-preserving distributed monitoring of visit quantities

 
: Kopp, Christine; Mock, Michael; May, Michael

:
Postprint urn:nbn:de:0011-n-2251048 (139 KByte PDF)
MD5 Fingerprint: 2828a3706e5c08706679332bd1906e12
© ACM 2012 This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Created on: 26.1.2013


Cruz, I. ; Association for Computing Machinery -ACM-, Special Interest Group on Spatial Information:
GIS 2012, 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Proceedings : November 6-9, 2012, Redondo Beach, California
New York: ACM, 2012
ISBN: 978-1-4503-1691-0
pp.438-441
International Conference on Advances in Geographic Information Systems (GIS) <20, 2012, Redondo Beach/Calif.>
English
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
trajectory stream analysis; privacy; local inference

Abstract
The organization and planning of services (e.g. shopping facilities, infrastructure) requires quantitative information about the number of customers and their frequency of visiting. In this paper we present a framework which enables the collection of quantitative visit information for arbitrary sets of locations in a distributed and privacy-preserving way. While trajectory analysis is typically performed on a central database requiring the transmission of sensitive personal movement information, the main principle of our approach is the local processing of movement data. Only aggregated statistics are transmitted anonymously to a central coordinator, which generates the global statistics. In this paper we present our approach including the methodical background that enables distributed data processing as well as the architecture of the framework.

: http://publica.fraunhofer.de/documents/N-225104.html