Verlagslink: http://arxiv.org/abs/2205.08440v2
Verlagslink DOI: 10.48550/arXiv.2205.08440
Titel: Moving smart contracts : a privacy preserving method for off-chain data trust
Sprache: Englisch
Autorenschaft: Tschirner, Simon 
Tripathi, Shashank Shekher  
Röper, Mathias 
Becker, Markus M. 
Skwarek, Volker  
Schlagwörter: Computer Science; Cryptography and Security; Software Engineering; Cluster Computing
Erscheinungsdatum: 17-Mai-2022
Verlag: Arxiv.org
Zeitschrift oder Schriftenreihe: De.arxiv.org 
Projekt: Blockchains und Knowledge Graphen zur Entwicklung und Erprobung von Qualitätskriterien und semantischen Metadaten für Forschungsdaten in der Plasmatechnologie 
Zusammenfassung: 
Blockchains provide environments where parties can interact transparently and securely peer-to-peer without needing a trusted third party. Parties can trust the integrity and correctness of transactions and the verifiable execution of binary code on the blockchain (smart contracts) inside the system. Including information from outside of the blockchain remains challenging. A challenge is data privacy. In a public system, shared data becomes public and, coming from a single source, often lacks credibility. A private system gives the parties control over their data and sources but trades in positive aspects as transparency. Often, not the data itself is the most critical information but the result of a computation performed on it. An example is research data certification. To keep data private but still prove data provenance, researchers can store a hash value of that data on the blockchain. This hash value is either calculated locally on private data without the chance for validation or is calculated on the blockchain, meaning that data must be published and stored on the blockchain -- a problem of the overall data amount stored on and distributed with the ledger. A system we called moving smart contracts bypasses this problem: Data remain local, but trusted nodes can access them and execute trusted smart contract code stored on the blockchain. This method avoids the system-wide distribution of research data and makes it accessible and verifiable with trusted software.
URI: http://hdl.handle.net/20.500.12738/13028
Einrichtung: Forschungs- und Transferzentrum Digitale Wirtschaftsprozesse 
Department Wirtschaftsingenieurwesen 
Fakultät Life Sciences 
Dokumenttyp: Vorabdruck (Preprint)
Hinweise zur Quelle: The work was funded by the German Federal Ministry of Education and Research (BMBF) under the grant marks 16QK03A and 16QK03C.
Sponsor / Fördernde Einrichtung: Bundesministerium für Bildung und Forschung 
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