Journal ArticleDOI
Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams
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TLDR
This paper has implemented a proof-of-concept for decentralized energy trading system using blockchain technology, multi-signatures, and anonymous encrypted messaging streams, enabling peers to anonymously negotiate energy prices and securely perform trading transactions.Abstract:
Smart grids equipped with bi-directional communication flow are expected to provide more sophisticated consumption monitoring and energy trading. However, the issues related to the security and privacy of consumption and trading data present serious challenges. In this paper we address the problem of providing transaction security in decentralized smart grid energy trading without reliance on trusted third parties. We have implemented a proof-of-concept for decentralized energy trading system using blockchain technology, multi-signatures, and anonymous encrypted messaging streams, enabling peers to anonymously negotiate energy prices and securely perform trading transactions. We conducted case studies to perform security analysis and performance evaluation within the context of the elicited security and privacy requirements.read more
Citations
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Book ChapterDOI
Potential for peer-to-peer trading of energy based on the home system of practice
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Patent
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References
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Proceedings ArticleDOI
Smart Grid Privacy via Anonymization of Smart Metering Data
TL;DR: The method described in this paper provides a 3rd party escrow mechanism for authenticated anonymous meter readings which are difficult to associate with a particular smart meter or customer.
Journal ArticleDOI
Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design
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Book ChapterDOI
Evaluating User Privacy in Bitcoin
TL;DR: This research examines the use of pseudonymity in the Bitcoin network, and the role that it plays in the development of trust and confidence in the system.
Proceedings ArticleDOI
Private memoirs of a smart meter
TL;DR: It is shown that even without a priori knowledge of household activities or prior training, it is possible to extract complex usage patterns from smart meter data using off-the-shelf statistical methods.