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Anonymization in intelligent surveillance systems

 
: Vagts, Hauke; Bier, Christoph; Beyerer, Jürgen

:
Postprint urn:nbn:de:0011-n-1971596 (63 KByte PDF)
MD5 Fingerprint: 91d29dfd2f9339c3546bb81723881ed0
© 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Erstellt am: 13.3.2012


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Communications Society; International Federation for Information Processing -IFIP-, Technical Committee 6, Communication Systems:
4th IFIP International Conference on New Technologies, Mobility and Security, NTMS 2011 : Paris, France, 7 - 10 February 2011
Piscataway/NJ: IEEE, 2011
ISBN: 978-1-4244-8705-9
ISBN: 978-1-4244-8704-2
4 S.
International Conference on New Technologies, Mobility and Security (NTMS) <4, 2011, Paris>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
Modern surveillance systems collect a massive amount of data. In contrast to conventional systems that store raw sensor material, modern systems take advantage of smart sensors and improvements in image processing. They extract relevant information about the observed objects of interest, which is then stored and processed during the surveillance process. Such high-level information is, e.g., used for situation analysis and can be processed in different surveillance tasks. Modern systems have become powerful, can potentially collect all kind of user information and make it available to any surveillance task. Hence, direct access to the collected high-level data must be prevented. Multiple approaches for anonymization exist, but they do not consider the special requirements of surveillance tasks. This work examines and evaluates existing metrics for anonymization and approaches for anonymization. Even though all kinds of data can be collected, position data is still the one with the highest demand. Hence, this work focuses on the anonymization of position data and proposes an algorithm that fulfills the requirements for anonymization in surveillance.

: http://publica.fraunhofer.de/dokumente/N-197159.html