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Ensuring Genuineness for Selectively Discolsed Confidential Data using Distributed Ledgers

 
: Lohr, M.; Hund, J.; Jürjens, J.; Staab, S.

:

IEEE 2nd International Conference on Blockchain 2019 : 14–17 July 2019 Atlanta, Georgia, USA
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-4693-5
ISBN: 978-1-7281-4694-2
S.477-482
International Conference on Blockchain <2, 2019, Atlanta/Ga.>
Englisch
Konferenzbeitrag
Fraunhofer ISST ()

Abstract
In railway incidents, data from sensors installed on railway tracks can help finding the cause of the incident and identifying the responsible parties. Since the data collected may contain business-relevant information, it is usually treated as confidential by the companies collecting it. However, this data can only be considered as evidence if it can be proven that the data is genuine and unaltered, even if it is only accessible for involved companies in the first place. In this paper, we present an approach to ensure the genuineness of confidential railway measurement data using distributed ledgers and describe an approach for selectively sharing parts of the data without compromising confidentiality or the verifiability of genuineness. We discuss the specific characteristics of rail wayside measurement data, existing approaches to ensure data genuineness and the necessary modifications to apply them to rail wayside measurement data. We also discuss how our approach can be generalized beyond the railway domain to show how distributed ledger-based approaches can be used to ensure the genuineness of confidential and selectively shared data.

: http://publica.fraunhofer.de/dokumente/N-575125.html