Traditional power grids have been the major source of electricity for several households and industries for a number of years. However, with that kind of supply, every member of the grid is affected whenever there is a fault on the transmission line connecting them. With that in mind, microgrids, usually powered by numerous distributed electrical sources, were introduced to curb this problem, and as such, energy users have also become producers themselves. Nonetheless, the generation of power by distributed sources brings about unpredictability on the network and, in essence, problems in energy sharing. Peer-to-peer (P2P) energy trading has several advantages and has been introduced to mitigate energy sharing problems. With networked energy trading comes the issue of trust, as several prosumers are concerned about their privacy and security in such environments. Therefore, this work leverages the advantages of blockchain in proposing a secure energy trading platform for all parties involved. Coupled with certificateless signcryption, an immutable energy trading market is designed, and its use case is applicable in smart cities. A thorough security analysis was performed, and the efficiency of our proposed solution is backed by numerical results.

The challenges of population management as urban density increases globally has compelled researchers and developers to consider more efficient means of managing resources in cities. Consequently, the smart city concept has emerged as a response to addressing the challenge of optimal resource utilization in urban centers. However, with digital technologies proliferating as key components of the solution, it is necessary to develop a digital identity solution for all components of the smart city environment. For completeness, the solution must encompass all entities including physical and intangible assets, processes, and most importantly, its residents. Consequently, a unified, distributed data integration and efficient analysis platform is required: the Digital City Operating System. In this work, we focus on a key component of digital city management in the form of secure identification of individual residents. We collect user attributes and securely transmit them to other system components for verification. Upon successful completion of the verification process, a digital identity is created for the applying resident and the set of transactions leading to the ID creation are stored in the blockchain. Our system is secure and can serve as the basis for the development of a digital infrastructure for smart city management.

In developing countries, funding is a significant obstacle to receiving higher education. Brilliant but needy students cannot complete their studies since their parents are unemployed and their countries’ economies are poor. As a result, the students’ talents are not harnessed to their full potential. In order to help students obtain higher education and harness their full potential, governments provide student loans to students in higher education. The government provides loans to students through the ministry of education. The students pay back the loan with interest when they start working. Governments have been the sole funders of student loans. The emergence of COVID-19 and the Russia-Ukraine war have resulted in a global economic crisis. Because of the global economic crisis, the government’s spending has increased. In order to help reduce the burden of government and thereby reduce spending, we intend to revolutionize the student loan program through blockchain and crowdsourcing. This work presents a blockchain-based crowdsourcing decentralized loan platform where investors will be brought on board to provide funds for students in higher education. The platform will allow students to apply for loans from investors through registered financial institutions. The students will pay back the loans with interest when they enter the workforce. The proposed platform will allow students to fund their education, investors will get interest on the money they invest, and governments can channel the money they put into student loan programs into other avenues. We perform a thorough security analysis and back the efficiency of our work with numerical results.

Because of the rise of cryptocurrencies and decentralized apps, blockchain technology has generated a lot of interest. Among these is the emergent blockchain-based crowdsourcing paradigm, which eliminates the centralized conventional mechanism servers in favor of smart contracts for task and reward allocation. However, there are a few crucial challenges that must be resolved properly. For starters, most reputation-based systems favor high-performing employees. Secondly, the crowdsourcing platform’s expensive service charges may obstruct the growth of crowdsourcing. Finally, unequal evaluation and reward allocation might lead to job dissatisfaction. As a result, the aforementioned issues will substantially impede the development of blockchain-based crowdsourcing systems. In this study, we introduce ExCrowd, a blockchain-based crowdsourcing system that employs a smart contract as a trustworthy authority to properly select workers, assess inputs, and award incentives while maintaining user privacy. Exploration-based crowdsourcing employs the hyperbolic learning curve model based on the conduct of workers and analyzes worker performance patterns using a decision tree technique. We specifically present the architecture of our framework, on which we establish a concrete scheme. Using a real-world dataset, we implement our model on the Ethereum public test network leveraging its reliability, adaptability, scalability, and rich statefulness. The results of our experiments demonstrate the efficiency, usefulness, and adaptability of our proposed system.

The Named Data Networking (NDN) architecture is a futuristic internet infrastructure that aims to deliver content efficiently. However, NDN is faced with the challenge of ensuring the privacy of both content and names. Traditional solutions have focused on encrypting and signing content before injecting the resultant ciphertext into the NDN platform to provide confidentiality and integrity. However, these solutions fail to protect content name privacy in critical applications such as the military and healthcare. To address this challenge, we propose Privacy-Preserving Data Sharing on Named Data Networking (PRIDN), which employs a combination of proxy re-encryption and symmetric mechanisms to secure both content and names. PRIDN offers several advantages over existing solutions. Firstly, it eliminates the need for subscribers to communicate with content publishers for decryption keys, reducing communication overhead and ensuring that content publishers do not need to be online all the time to respond to key generation requests. Second, the proxy re-encryption mechanism prevents replication of ciphertexts, thus avoiding multiple instances of the same content in the network. Lastly, PRIDN also protects sensitive information in content names, preventing user profiling and censorship. Simulation results from ndnSIM and MIRACL libraries demonstrate that PRIDN reduces content retrieval time on NDN. A crypto-verification tool, Verifpal, shows that the proposed protocols are secure for real-world deployment.

Key-policy attribute-based encryption (KP-ABE) is the cryptographic primitive which enables fine grained access control while still providing end-to-end encryption. Although traditional encryption schemes can provide end-to-end encryption, users have to either share the same decryption keys or the data have to be stored in multiple instances which are encrypted with different keys. Both of these options are undesirable. However, KP-ABE can provide less key overhead compared to the traditional encryption schemes. While there are a lot of KP-ABE schemes, none of them simultaneously supports multiuse of attributes, adaptive security, monotone span programs, and static security assumption. Hence, we propose a fully secure KP-ABE scheme for monotone span programs in prime-order group. This scheme uses selective security proof techniques to obtain the requisite ingredients for full security proof. This strengthens the correlation between selective and full security models and enables the transition of the best qualities in selective security models to fully secure systems. The security proof is based on decisional linear assumption and three-party Diffie–Hellman assumption.