During the last decade, distributed ledger solutions such as blockchain have gained significant attention due to their decentralized, immutable, and verifiable features. However, the public availability of data stored on the blockchain and its link to users may raise privacy and security issues. In some cases, addressing these issues requires blockchain data to be secured with mechanisms that allow on-demand (as opposed to full) disclosure. In this paper, we give a comprehensive overview of blockchain privacy and security requirements, and detail how existing mechanisms answer them. We provide a taxonomy of current attacks together with related countermeasures. We present a thorough comparative analysis based on various parameters of state-of the-art privacy and security mechanisms, we provide recommendations to design secure and privacy-aware blockchain, and we suggest guidelines for future research.

Energy Transition (ET) needs actors to perform independent actions on multiple levels of governance. These actors may need to write and read their data, and at the same time they want to protect their data from unauthorized access. This is particularly the case for positive energy districts (PED), a growing trend in the EU that requires actors to perform, write and read operations on a neighborhood scale where governance competences are typically absent. This paper presents a decentralized privacy-aware data management framework that enables actors to store, read, and modify data in PEDs. Our framework design integrates blockchain with a Distributed Hash Table (DHT), role-based access control, ring signature, and different encryption techniques. The proposed framework stores encrypted data on the DHT, and metadata and hash key are sent to the blockchain, which allows the data owner to keep track of their data. The proposed framework components handle multi-level data access in PEDs and enable data security at run-time. Moreover, we show security and privacy analysis and performance evaluation in time overhead. The results show that the proposed solution is effective, secure, and scalable.

Data privacy protection and secure sharing are the main issues faced by smart healthcare Internet of Things (IoT) systems. In medical uses, patient health information is frequently kept in the cloud, which limits the user’s ability to entirely control their data. Additionally, standard encryption keys do not sufficiently mitigate the risks posed by malicious entities like compromised cloud service providers. To address these issues, blockchain technology, combined with Internet of Medical Things (IoMT) can securely safeguard patient medical records through a peer-to-peer, secure, and collective ledger. Therefore, we propose a novel IoT-driven architecture that leverages blockchain technology to protect patient medical files from tampering and unauthorized access. This architecture integrates patient medical files with blockchain and is enhanced by a combination of bidirectional long short-term memory (BiLSTM) networks and convolutional neural networks (CNN). Utilizing blockchain for the transmission of encrypted data significantly strengthens data security and minimizes the risk of data breaches. The process of generating encryption and decryption keys through a coupled CNN and BiLSTM ensures the robustness and uniqueness of these keys. Additionally, the selection of the best key is performed using the gradient descent optimization algorithm (GDOA), which demonstrates the effectiveness and efficiency of the encryption and decryption process. We also compare the implementation of our model with existing technologies, assessing its performance based on various metrics, including restoration efficiency, response time, record time, key generation time, encryption time, decryption time, turnaround time, and overall running time. Our proposed method is confirmed to be more effective than current techniques in terms of these performance metrics.

Indoor Air Quality monitoring is a major asset to improving quality of life and building management. Today, the evolution of embedded technologies allows the implementation of such monitoring on the edge of the network. However, several concerns need to be addressed related to data security and privacy, routing and sink placement optimization, protection from external monitoring, and distributed data mining. In this paper, we describe an integrated framework that features distributed storage, blockchain-based Role-based Access Control, onion routing, routing and sink placement optimization, and distributed data mining to answer these concerns. We describe the organization of our contribution and show its relevance with simulations and experiments over a set of use cases.

Blockchain technology gained popularity thanks to its decentralized and transparent features. However, it suffers from a lack of privacy as it stores data publicly and has difficulty to handle data updates due to its main feature known as immutability. In this paper, we propose a decentralized data storage and access framework that combines blockchain technology with Distributed Hash Table (DHT), a role-based access control model, and multiple encryption mechanisms. Our framework stores metadata and DHT keys on the blockchain, while encrypted data is managed on the DHT, which enables data owners to control their data. It allows authorized actors to store and read their data in a decentralized storage system. We design REST APIs to ensure interoperability over the Web. Concerning data updates, we propose a pointer system that allows data owners to access their update history, which solves the issue of data updates while preserving the benefits of using the blockchain. We illustrate our solution with a wood supply chain use case and propose a traceability algorithm that allows the actors of the wood supply chain to trace the data and verify product origin. Our framework design allows authorized users to access the data and protects data against linking, eavesdropping, spoofing, and modification attacks. Moreover, we provide a proof-of-concept implementation, security and privacy analysis, and evaluation for time consumption and scalability. The experimental results demonstrate the feasibility, security, privacy, and scalability of the proposed solution.