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The real first class? Inferring confidential corporate mergers and government relations from air traffic communication

Abstract:

This paper exploits publicly available aircraft meta data in conjunction with unfiltered air traffic communication gathered from a global collaborative sensor network to study the privacy impact of large-scale aircraft tracking on governments and public corporations. First, we use movement data of 542 verified aircraft used by 113 different governments to identify events and relationships in the real world. We develop a spatio-temporal clustering method which returns 47 public and 18 non-publ...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1109/EuroSP.2018.00016

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
ORCID:
0000-0002-1936-0933
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Publisher:
IEEE Publisher's website
Pages:
107-121
Publication date:
2018-07-09
Acceptance date:
2017-11-21
DOI:
Pubs id:
pubs:821729
URN:
uri:3df6e2a7-f82a-4119-9326-6c658c559b8e
UUID:
uuid:3df6e2a7-f82a-4119-9326-6c658c559b8e
Local pid:
pubs:821729
ISBN:
978-1-5386-4228-3

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