Smart Grid Management using Blockchain: Future Scenarios and Challenges

Decentralized management and coordination of energy systems are emerging trends facilitated by the uptake of the Internet of Things and Blockchain offering new opportunities for more secure, resilient, and efficient energy distribution. Even though the use of distributed ledger technology in the energy domain is promising, the development of decentralized smart grid management solutions is in the early stages. In this paper, we define a layered architecture of a blockchain-based smart grid management platform featuring energy data metering and tamper-proof registration, business enforcement via smart contracts, and Oracle-based integration of high computational services supporting the implementation of future grid management scenarios. Three such scenarios are discussed from the perspective of their implementation using the proposed blockchain platform and associated challenges: peer to peer energy trading, decentralized management, and aggregation of energy flexibility and operation of community oriented Virtual Power Plants.Comment: Accepted and presented at: 19th RoEduNet Conference: Networking in Education and Research, December 11-12, 202

Blockchain for secure decentralized energy management of multi-energy system using state machine replication

Decentralized energy management can preserve the privacy of individual energy systems while mitigating computational and communication burdens. However, most decentralized energy management methods are partially decentralized and cannot ensure information exchange security. Therefore, this paper provides a secure fully decentralized energy management by using blockchain. First, a fully decentralized energy management framework using the optimality condition decomposition (OCD) is provided, in which individual energy system operators only exchange the boundary information with their peers rather than submitting proprietary information to a centralized system operator. Then, an asynchronous mechanism is proposed for updating the information exchange in OCD, enabling the proposed decentralized management to work under potential communication latency or interruption. Furthermore, the blockchain-based framework with state machine replication (SMR) based consensus algorithm is provided to safeguard the information exchange among individual energy systems in a secure and tamper-proof manner. The proposed decentralized energy management is tested on a multi-energy system with seven subsystems and a real-world multi-energy system in North China. The numerical results demonstrate the effectiveness of the proposed method in privacy protection and data security enhancement. The proposed method can prevent the cost increase caused by cheating activities, which in some subsystems can reach 17.6%. Additionally, the proposed fully decentralized method outperforms the partially decentralized method by 37.7% in reducing computation time. Also demonstrated are the computational precision, scalability and adaptability of the proposed method

Secure Cloud SDN Educational Management with Internet + Learning Management System

The education management model refers to the system and processes that colleges and universities use to manage and oversee their academic programs and operations. However, with the advent of digital technologies, there has been a growing trend towards the Internet+ college education management model, which integrates digital technologies into all aspects of college education management. This model includes the use of online learning platforms and tools, such as learning management systems (LMS), to deliver courses and manage student progress. It also includes the use of digital technologies for administrative tasks such as admissions, enrolment, and financial aid. However, the educational management model is subjected to the challenge of security for educational data management. Hence, this paper constructed a secure framework model of the Ethereum SDN Cloud Architecture (ESDNarc). The ESDNarc model uses the Software-defined Network (SDN) for the decentralized management of the network, secure transactions, and improved efficiency. The ESDNarch model incorporates the SDN with the cryptography scheme the secure the data. The constructed model uses the double-hashing Elliptical Curve Cryptography (DHECC) for the data stored in the Ethereum blockchain. The performance of the constructed model is evaluated with the KDD data set. Simulation analysis stated that ESDNarch significantly increases the data security in the cloud model for the attacks in the network

Blood Management System Using Blockchain

Blood is a crucial constituent within the human body that is irreplaceable for human life, it supplies supplements and oxygen to all the cells, due to this fundamental part, the requirement for a decentralized blood bank has been introduced in this paper. Manual frameworks as compared to computerized frameworks are time-consuming, exorbitant, and may regularly contain human errors. Moreover, they are helpless to the single point of disappointment issue due to centralization and may lack privacy and security features. This research paper explores the usage of a blood management system based on blockchain technology. The current blood management systems confront challenges such as donor-recipient anonymity, traceability, and straightforwardness. These issues can be tended to by utilizing blockchain, which gives a decentralized and secure database for data administration. The suggested system makes use of blockchain to handle and preserve data from blood banks, such as donor details, blood type, and availability.

Privacy-Preserving Statistical Analysis of Health Data Using Paillier Homomorphic Encryption and Permissioned Blockchain

Blockchain is a decentralized and peer-to-peer ledger technology that adds transparency, traceability, and immutability to data. It has shown great promise in mitigating the interoperability problem and privacy concerns in the de facto electronic health record anagement systems and has recently received increasing attention from the healthcare industry. Several blockchain-based and decentralized health data management mechanisms have been proposed to improve the quality of care delivery to patients. Apart from care delivery, health data has other important applications, such as education, regulation, research, public health improvement, and policy sup- port. However, existing privacy acts prohibit health institutions and providers from sharing patients\u27 data with third parties. Therefore, research institutions that con- duct research on private health data need a secure system that provides accurate analysis results while preserving patient privacy and minimizing the risks of data breaches. In this thesis, We propose a novel privacy-preserving method for statis- tical analysis of health data. We leveraged the blockchain technology and Paillier encryption algorithm to increase the accuracy of data analysis while preserving the privacy of patients. Smart contracts were used to carry out mathematical operations on the encrypted records in a secure manner. We were able to successfully deploy the proposed scheme on Hyperledger Fabric, a permissioned and consortium blockchain platform. Compared to the previous works, the proposed model enjoys the bene ts of a distributed blockchain-based environment, which include higher availability and enhanced data security. The experimental results show the feasibility of this method with a reasonable amount of time for regular queries. Blockchain is a decentralized and peer-to-peer ledger technology that adds transparency, traceability, and immutability to data. It has shown great promise in mitigating the interoperability problem and privacy concerns in the de facto electronic health record anagement systems and has recently received increasing attention from the healthcare industry. Several blockchain-based and decentralized health data management mechanisms have been proposed to improve the quality of care delivery to patients. Apart from care delivery, health data has other important applications, such as education, regulation, research, public health improvement, and policy sup- port. However, existing privacy acts prohibit health institutions and providers from sharing patients\u27 data with third parties. Therefore, research institutions that con- duct research on private health data need a secure system that provides accurate analysis results while preserving patient privacy and minimizing the risks of data breaches. In this thesis, We propose a novel privacy-preserving method for statis- tical analysis of health data. We leveraged the blockchain technology and Paillier encryption algorithm to increase the accuracy of data analysis while preserving the privacy of patients. Smart contracts were used to carry out mathematical operations on the encrypted records in a secure manner. We were able to successfully deploy the proposed scheme on Hyperledger Fabric, a permissioned and consortium blockchain platform. Compared to the previous works, the proposed model enjoys the bene ts of a distributed blockchain-based environment, which include higher availability and enhanced data security. The experimental results show the feasibility of this method with a reasonable amount of time for regular queries

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems