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Privacy-preserving distributed monitoring of visit quantities

By Christine Kopp, Michael Mock and Michael May

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

Topics: trajectory stream analysis, privacy, local inference
Year: 2012
OAI identifier: oai:fraunhofer.de:N-225104
Provided by: Fraunhofer-ePrints
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