17,775 research outputs found
End-to-End Privacy for Open Big Data Markets
The idea of an open data market envisions the creation of a data trading
model to facilitate exchange of data between different parties in the Internet
of Things (IoT) domain. The data collected by IoT products and solutions are
expected to be traded in these markets. Data owners will collect data using IoT
products and solutions. Data consumers who are interested will negotiate with
the data owners to get access to such data. Data captured by IoT products will
allow data consumers to further understand the preferences and behaviours of
data owners and to generate additional business value using different
techniques ranging from waste reduction to personalized service offerings. In
open data markets, data consumers will be able to give back part of the
additional value generated to the data owners. However, privacy becomes a
significant issue when data that can be used to derive extremely personal
information is being traded. This paper discusses why privacy matters in the
IoT domain in general and especially in open data markets and surveys existing
privacy-preserving strategies and design techniques that can be used to
facilitate end to end privacy for open data markets. We also highlight some of
the major research challenges that need to be address in order to make the
vision of open data markets a reality through ensuring the privacy of
stakeholders.Comment: Accepted to be published in IEEE Cloud Computing Magazine: Special
Issue Cloud Computing and the La
Privacy-Preserving Transactive Energy Management for IoT-aided Smart Homes via Blockchain
With the booming of smart grid, The ubiquitously deployed smart meters
constitutes an energy internet of things. This paper develops a novel
blockchain-based transactive energy management system for IoT-aided smart
homes. We consider a holistic set of options for smart homes to participate in
transactive energy. Smart homes can interact with the grid to perform vertical
transactions, e.g., feeding in extra solar energy to the grid and providing
demand response service to alleviate the grid load. Smart homes can also
interact with peer users to perform horizontal transactions, e.g., peer-to-peer
energy trading. However, conventional transactive energy management method
suffers from the drawbacks of low efficiency, privacy leakage, and single-point
failure. To address these challenges, we develop a privacy-preserving
distributed algorithm that enables users to optimally manage their energy
usages in parallel via the smart contract on the blockchain. Further, we design
an efficient blockchain system tailored for IoT devices and develop the smart
contract to support the holistic transactive energy management system. Finally,
we evaluate the feasibility and performance of the blockchain-based transactive
energy management system through extensive simulations and experiments. The
results show that the blockchain-based transactive energy management system is
feasible on practical IoT devices and reduces the overall cost by 25%.Comment: To appea
Lightweight Blockchain Framework for Location-aware Peer-to-Peer Energy Trading
Peer-to-Peer (P2P) energy trading can facilitate integration of a large
number of small-scale producers and consumers into energy markets.
Decentralized management of these new market participants is challenging in
terms of market settlement, participant reputation and consideration of grid
constraints. This paper proposes a blockchain-enabled framework for P2P energy
trading among producer and consumer agents in a smart grid. A fully
decentralized market settlement mechanism is designed, which does not rely on a
centralized entity to settle the market and encourages producers and consumers
to negotiate on energy trading with their nearby agents truthfully. To this
end, the electrical distance of agents is considered in the pricing mechanism
to encourage agents to trade with their neighboring agents. In addition, a
reputation factor is considered for each agent, reflecting its past performance
in delivering the committed energy. Before starting the negotiation, agents
select their trading partners based on their preferences over the reputation
and proximity of the trading partners. An Anonymous Proof of Location (A-PoL)
algorithm is proposed that allows agents to prove their location without
revealing their real identity. The practicality of the proposed framework is
illustrated through several case studies, and its security and privacy are
analyzed in detail
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