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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.

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Citations
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Tackling Data Inefficiency: Compressing the Bitcoin Blockchain

TL;DR: The proposed compressible blockchain architecture is used to reduce the size of the blockchain by up to 93%, facilitating secure blockchain-based applications even on resource-constrained thin clients such as IoT devices.
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Design and Field Implementation of Blockchain Based Renewable Energy Trading in Residential Communities

TL;DR: In this article, a peer-to-peer (P2P), blockchain based energy trading market platform for residential communities with the objective of reducing overall community peak demand and household electricity bills is proposed.
Proceedings ArticleDOI

User-Centric Peer-to-Peer Energy Trading Mechanisms for Residential Microgrids

TL;DR: Two user-centric pricing strategies for facilitating peer-to-peer energy trading in residential microgrids are proposed: Unified Pricing (UP) strategy and Identified Pricing (IP) strategy that aim to maximize the profit of small-scale distributed energy resources owners while taking into account the user's life convenience and solar photovoltaic uncertainty.
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Incentivizing Resource Cooperation for Blockchain Empowered Wireless Power Transfer in UAV Networks

TL;DR: A contract theory based resource cooperation scheme is designed to motivate the UAVs to participate in wireless power transfer, and to incentivize the APs to contribute their resources in cooperatively verifying the timeout energy micro-transactions.
Proceedings ArticleDOI

Decentralized blockchain flexibility system for Smart Grids: Requirements engineering and use cases

TL;DR: Results from the ongoing project eDREAM (enabling new Demand REsponse Advanced, Market oriented and Secure technologies, solutions and business models) are presented with some extent on requirements and use cases related a decentralized flexibility system based on ledger technologies.
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

TL;DR: In this paper, the effectiveness of providing paper-based energy-use/saving information with electronic feedback of energy-consumption via smart meters and displays, or "energyconsumption indicators" (ECI) is reviewed.
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.
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