The Internet of Things (IoT) is producing significant amounts of data. Protecting this data from adversaries is therefore a prominent field of research. This paper conducts a review of the current state-of-the-art in the field of IoT integrated with Blockchain (BC) and Machine Learning (ML). The review focuses on the use of privacy and performance metrics to evaluate the effectiveness of these latest solutions. We first provide an overview of related works to have an understanding of what has been done. Then, we present five important privacy and performance metrics that have been used in the review. We then provide a detailed evaluation of state-of-the-art solutions. Finally, we identify and present open problems that need to be addressed in the form of future research directions. This paper provides valuable insights for researchers interested in improving privacy and performance in IoT applications, and opportunities for future research.

The Internet of Things industry is expanding rapidly. However, many security breaches occur, and privacy is often at stake in traditional IoT networks. These centralized systems will not be able to cope with dynamically changing environments. In light of these risks, it is crucial to prevent and minimize the chances of attacks occurring. Researchers have attempted to use blockchain for IoT security to ensure data consistency and availability. Fully public decentralized solutions for IoT still face data breaches. On the other hand, machine learning models detect potential attacks to create an effective defense system. This paper surveys state-of-the-art works looking to integrate blockchain with machine learning to protect data management for the IoT. Before exploring the various implementations, an analysis of multiple surveys that dive deeper into such integrations is made; then, five different blockchain and machine learning integrations. Many papers need a complete security analysis, and the experiments are limited. From studying the relevant integrations, this article presents a new scheme to protect IoT data using the DQNSB consensus algorithm to train a global model by distributing machine learning tasks, leveraging transparency to guarantee security.

Double Auctions are mechanisms to trade commodities such as electricity or parts of the wireless spectrum at optimal prices. Bidders and sellers simultaneously submit quantity-price pairs to an auctioneer, denoting the quantity they want to buy or sell at specific prices. The auctioneer aggregates the offers into demand and supply curves to compute the auction result by finding the intersection between supply and demand. In this way, commodities exchange owners in an economically efficient manner, driven by the market. In an ideal scenario, the auctioneer is a trusted third party that does not abuse the information they gain. However, in reality, offers reveal usage patterns of customers, such as electricity usage, or may be used by the auctioneer for their economic gain as insider information. The auctioneer also has opportunities to manipulate results of which there are real-life allegations in electricity trading or advertisement auctions. These concerns call for solutions that conduct the auction in a privacy-preserving and verifiable way while not compromising the auction functionality or economic efficiency.

Proposed solutions for privacy-preserving and verifiable double auctions offer confidentiality but do not allow participants to verify results independently or vice versa without interaction of participants in the full auction procedure. We specifically focus on electricity trading to design a solution covering the above concerns. To the best of our knowledge, we propose the first privacy-preserving and verifiable double auction scheme without interactivity of all participants, tailored to electricity trading on (inter)national exchanges. Using cryptographic schemes, including Homomorphic encryption, Commitment schemes, and Zero-knowledge-proofs, we propose a solution to establish a double auction protocol that preserves privacy and allows for verification.

The upcoming IEEE 802.1CB standard aims to solve performance and reliability issues in Time-Sensitive Networking (TSN). Mission-critical systems often use these standards for communication in automotive, industrial, and avionic networks. However, researchers did not sufficiently investigate the security risks and possible mitigation solutions to this introduced standard. This limited knowledge is a problem as Cyber-Physical Systems (CPS) are mission-critical and time-sensitive, and any unexpected failure of these systems could endanger lives.

To attain a complete overview of the security risks of IEEE 802.1CB, we use an improved STRIDE model for Cyber-Physical Systems. We then design and create a hardware test-bed for Time-Sensitive Networking to prove the feasibility of the identified security risks. Finally, we implement attacks for the identified security risks in IEEE 802.1CB and analyze their impact by running experiments on the hardware test-bed. The results show that some of the identified security risks significantly impact the network’s reliability as we successfully execute a Replay attack and a Denial of Service attack. However, the hardware switches provide only limited functionality of the IEEE 802.1CB specification. Therefore, we could not verify the attacks against all identified security risks.

These results show that this networking standard is not ready to be used in Cyber-Physical Systems as the impact of the identified security risks is too significant. On this basis, we recommend additional research and improvements to the IEEE 802.1CB standard and mitigation solutions.

The Internet of Things (IoT) is a technology used in applications varying from home- and industrial automation to medical devices, smart vehicles, fitness trackers and many more. Such IoT networks often consist of incredibly resource-constrained devices, and are known as Low-power and Lossy Networks (LLNs). The Routing Protocol for LLNs (RPL) aims to provide a routing standard for such networks. Due to the tough performance constraints, RPL is unable to provide strong security guarantees. Many researchers are posing attacks against RPL-based networks, and with the increasing number of implementing devices, it is important that research is done to ensure message integrity and network reliability. In this paper we concern ourselves specifically with collusion attacks. We propose Hop-Count Reachability (HCR), a mitigating method against the coordinated blackhole attack. In HCR, leaf nodes periodically ping the root node with DAO messages. If the root node is reachable, it will reply with a DAO-ACK, upon which the leaf node sleeps for a period of time. When the number of missed ACKs in a certain time frame exceeds a certain threshold, the affected node may identify the attack and mitigate it by selecting a new parent. HCR may increase control packet overhead anywhere between 1.6 and 25\% depending on the chosen parameters, and successfully mitigates the coordinated blackhole attack in all scenarios where affected nodes can choose an (eventually) unaffected parent.

The sharing of medical data is becoming ever more important. More and more health-related data is being generated everyday and as will be shown later, its primary as well as its secondary usage brings many benefits to the healthcare system. However, medical data systems are not fail proof and are often the target of cyber-attacks, compromising the privacy of patients and the availability of the system. In fact, there are several security and privacy parameters which a medical data sharing system should ideally adhere to. These include strong authentication and unforgeability, integrity and confidentiality of data, proper consent management and access control mechanisms, availability of data and services, identity anonymity, data anonymity and unlinkability. With the emergence of blockchain technology a new possibility has emerged to realize a secure and trusted medical data sharing system across different institutes, where patients are in control of their data. Through fundamental features of blockchain, like digital signatures, a peer-to-peer network, a distributed and immutable data structure, decentralized consensus, off-chain storage and smart contracts the various security and privacy goals of a medical data sharing system can be met. Primarily these features do a successful job in addressing the security related requirements of a medical data sharing system. Authentication and unforgeability are provided through digital signatures, integrity and confidentiality are met through digital signatures and the immutable ledger, consent management and access control can be implemented with smart contracts and finally availability is achieved through the peer-to-peer nature of the blockchain. Privacy requirements are not handled well through these inherent features and require additional techniques to be met. Identity mixers protocols, relying on zero-knowledge proofs, can be used to achieve identity anonymity and unlinkability.

Information-centric Networking (ICN) is the revolution of the internet due to its many benefit over the current internet infrastructure, namely caching, location-independent routing, and data-centric security. ICN ensures seamless data transfer due to its content-centric nature. Due to its content-centric nature, it is a must to ensure that security and privacy are in place before a shift to ICN takes place. There are three ways to make a shift to ICN: 1) Underlay, deploying ICN under the IP; 2) Overlay, deploying ICN over IP; 3) Hybrid, deploying ICN with IP. In this paper, we center on hybrid ICN architectures, two architectures are analyzed to examine their security and privacy features. The two architectures discussed are Hybrid Information-Centric Networking (hICN), and Content Centric Inter-Networking (CONET). Background and related works, on ICN as a whole, and the two architectures are given, followed by the analysis of the two. Features namely Availability, Access Control, Non-repudiation, Integrity, and Authentication are studied under security. Whereas in privacy, Anonymity, Confidentiality, and Unlinkability are studied. The paper presents the implementation and the relevance of these features to ICN architecture. After analyzing these traits separately in the two architectures, a comparison is drawn between them. Lastly, the conclusions are formed with a discussion of future works.

Patient monitoring and clinical trial management generate continuous large volumes of healthcare data, causing the healthcare industry to be a data-intensive domain. Existing data storage techniques and access mechanics in the healthcare domain exhibit several challenges related to data security, patient privacy, and interoperability. Blockchain technology, together with the support from smart contracts, is considered a proper facilitator for secure and efficient healthcare data storage and sharing. Blockchain technology has unique features, such as decentralization, trustlessness, immutability, traceability, and transparency. Ongoing efforts show promising results considering blockchain technology to improve different aspects of healthcare data sharing and data management. However, all of these initiatives are still in the initial stages and lack technical details. This specifically pertains to the lack of investigation and evaluation of existing storage methods and techniques. Therefore, we will provide a comprehensive evaluation of different storage methods and techniques in ongoing efforts. We introduce a storage architecture for a possible blockchain-based healthcare system. The research proposals are evaluated considering the different storage methods and techniques. Additionally, we investigate the presence of six key requirements to provide a good storage solution. These requirements consist of data location, storage security, access mechanisms, third parties, storage purpose, operations and data integrity. None of the investigated systems meet all six requirements identified in this study. Therefore, we have proposed a storage architecture of our own.