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Register a new userA Secure and Efficient Direct Power Load Control Framework Based on Blockchain
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Added by
Ali Dorri
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Security and privacy in Direct Load Control (DLC) is a fundamental challenge in smart grids. In this paper, we propose a blockchain-based framework to increase security and privacy of DLC. We propose a method whereby participating nodes share their data with the distribution company in an anonymous and secure manner. To reduce the associated overhead for data dissemination, we propose a hash-based transaction generation method. We also outline the DLC process for managing the load in consumer site. Qualitative analysis demonstrates the security and privacy of the proposed method.
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Federated learning (FL) has emerged as a promising master/slave learning paradigm to alleviate systemic privacy risks and communication costs incurred by cloud-centric machine learning methods. However, it is very challenging to resist the single point of failure of the master aggregator and attacks from malicious participants while guaranteeing model convergence speed and accuracy. Recently, blockchain has been brought into FL systems transforming the paradigm to a decentralized manner thus further improve the system security and learning reliability. Unfortunately, the traditional consensus mechanism and architecture of blockchain systems can hardly handle the large-scale FL task due to the huge resource consumption, limited transaction throughput, and high communication complexity. To address these issues, this paper proposes a two-layer blockchaindriven FL framework, called as ChainsFL, which is composed of multiple subchain networks (subchain layer) and a direct acyclic graph (DAG)-based mainchain (mainchain layer). In ChainsFL, the subchain layer limits the scale of each shard for a small range of information exchange, and the mainchain layer allows each shard to share and validate the learning model in parallel and asynchronously to improve the efficiency of cross-shard validation. Furthermore, the FL procedure is customized to deeply integrate with blockchain technology, and the modified DAG consensus mechanism is proposed to mitigate the distortion caused by abnormal models. In order to provide a proof-ofconcept implementation and evaluation, multiple subchains base on Hyperledger Fabric are deployed as the subchain layer, and the self-developed DAG-based mainchain is deployed as the mainchain layer. The experimental results show that ChainsFL provides acceptable and sometimes better training efficiency and stronger robustness compared with the typical existing FL systems.
IoT devices have been adopted widely in the last decade which enabled collection of various data from different environments. The collected data is crucial in certain applications where IoT devices generate data for critical infrastructure or systems whose failure may result in catastrophic results. Specifically, for such critical applications, data storage poses challenges since the data may be compromised during the storage and the integrity might be violated without being noticed. In such cases, integrity and data provenance are required in order to be able to detect the source of any incident and prove it in legal cases if there is a dispute with the involved parties. To address these issues, blockchain provides excellent opportunities since it can protect the integrity of the data thanks to its distributed structure. However, it comes with certain costs as storing huge amount of data in a public blockchain will come with significant transaction fees. In this paper, we propose a highly cost effective and reliable digital forensics framework by exploiting multiple inexpensive blockchain networks as a temporary storage before the data is committed to Ethereum. To reduce Ethereum costs,we utilize Merkle trees which hierarchically stores hashes of the collected event data from IoT devices. We evaluated the approach on popular blockchains such as EOS, Stellar, and Ethereum by presenting a cost and security analysis. The results indicate that we can achieve significant cost savings without compromising the integrity of the data.
The main problem faced by smart contract platforms is the amount of time and computational power required to reach consensus. In a classical blockchain model, each operation is in fact performed by each node, both to update the status and to validate the results of the calculations performed by others. In this short survey we sketch some state-of-the-art approaches to obtain an efficient and scalable computation of smart contracts. Particular emphasis is given to sharding, a promising method that allows parallelization and therefore a more efficient management of the computational resources of the network.
Blockchain is increasingly being used to provide a distributed, secure, trusted, and private framework for energy trading in smart grids. However, existing solutions suffer from lack of privacy, processing and packet overheads, and reliance on Trusted Third Parties (TTP). To address these challenges, we propose a Secure Private Blockchain-based (SPB) framework. SPB enables the energy producers and consumers to directly negotiate the energy price. To reduce the associated packet overhead, we propose a routing method which routes packets based on the destination Public Key (PK). SPB eliminates the need for TTP by introducing atomic meta-transactions. The two transactions that form a meta-transaction are visible to the blockchain participants only after both of them are generated. Thus, if one of the participants does not commit to its tasks in a pre-defined time, then the energy trade expires and the corresponding transaction is treated as invalid. The smart meter of the consumer confirms receipt of energy by generating an Energy Receipt Confirmation (ERC). To verify that the ERC is generated by a genuine smart meter, SPB supports authentication of anonymous smart meters which in turn enhances the privacy of the meter owner. Qualitative security analysis shows the resilience of SPB against a range of attacks.
The Internet of Vehicles (IoV) is an application of the Internet of things (IoT). It faces two main security problems: (1) the central server of the IoV may not be powerful enough to support the centralized authentication of the rapidly increasing connected vehicles, (2) the IoV itself may not be robust enough to single-node attacks. To solve these problems, this paper proposes SG-PBFT: a secure and highly efficient PBFT consensus algorithm for Internet of Vehicles, which is based on a distributed blockchain structure. The distributed structure can reduce the pressure on the central server and decrease the risk of single-node attacks. The SG-PBFT consensus algorithm improves the traditional PBFT consensus algorithm by using a score grouping mechanism to achieve a higher consensus efficiency. The experimental result shows that our method can greatly improve the consensus efficiency and prevent single-node attacks. Specifically, when the number of consensus nodes reaches 1000, the consensus time of our algorithm is only about 27% of what is required for the state-of-the-art consensus algorithm (PBFT). Our proposed SG-PBFT is versatile and can be used in other application scenarios which require high consensus efficiency.
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