In cryptocurrencies, privacy of users is preserved using pseudonymity . However, it has been shown that pseudonymity does not result in anonymity if a user's transactions are linkable. This makes cryptocurrencies vulnerable to deanonymization attacks. The current solutions proposed in the literature suffer from at least one of the following issues: (1) requiring a trusted third–party entity, (2) poor performance, and (3) incompatible with the standard structure of cryptocurrencies. In this article, we propose Unlinkable Coin (UCoin), a secure mix–based approach to address these issues. In UCoin, the link between the input (payer) and output (payee) addresses in a transaction is broken. This is done by mixing the transactions of multiple users into a single aggregated transaction in which the output addresses have been secretly shuffled. In our protocol design, we first develop HDC–net, a secure shuffling protocol that enables a group of users to anonymously publish their data. Then, we deploy the proposed HDC–net protocol in the UCoin architecture (as a mixing unit) to generate the aggregate transactions. We show that UCoin (1) does not rely on a trusted third–party, (2) can mix 50 transactions in 6.3 seconds that is 18% faster than the current solutions, and (3) is fully compatible with the architecture of cryptocurrencies.

Blockchain has attracted considerable attention as a solution to the challenges of privacy, security and decentralisation for many applications. However, these characteristics of the blockchain result in ever growing ledger size, which is one of the major barriers to blockchain adoption in large-scale networks such as the Internet of Things (IoT). In this paper, we propose Multi-Level Distributed Caching (MLDC) for blockchain storage optimisation which reduces data replication based on data access pattern. MLDC divides nodes into storage classes (SCs) by their node availability, and assigns each SC a different Access Frequency (AF) to remove data from the local storage. Over time, each node only stores frequently accessed data, so MLDC can reduce the total storage cost by 83% compared to conventional blockchain systems, while maintaining blockchain consistency and data availability with a slight increase in network overhead and data query delay.

In recent years, blockchain technology has received tremendous attention. Blockchain users are known by a changeable public key (PK) that introduces a level of anonymity; however, studies have shown that anonymized transactions can be linked to deanonymize the users. Most of the existing studies on user deanonymization focus on monetary applications; however, the blockchain has received extensive attention in nonmonetary applications such as the Internet of Things (IoT). In this article, we study the impact of deanonymization on the IoT-based blockchain. We populate a blockchain with data of smart home devices and then apply machine learning algorithms in an attempt to classify the transactions to a particular device that, in turn, risks the privacy of the users. Two types of attack models are defined: 1) informed attacks: where attackers know the type of devices installed in a smart home and 2) blind attacks: where attackers do not have this information. We show that machine learning algorithms can successful classify the transactions with 90% accuracy. To enhance the anonymity of the users, we introduce multiple obfuscation methods which include combining multiple packets into a transaction, merging ledgers of multiple devices, and delaying transactions. The implementation results show that these obfuscation methods significantly reduce the attack success rates to 20%–30% and, thus, enhance the user privacy.

Distribution, security, and immutability have led to the great success of blockchain in many applications, while contributing to major increases in ledger size. The storage challenge is one of the major barriers to the adoption of blockchain in the Internet of Things (IoT), which consists of many resource constrained devices. In this paper, we propose Multi-Level Distributed Caching (MLDC) for blockchain storage optimisation which reduces data replication based on data access patterns in a decentralised manner. For storage optimisation of data-centric blockchains, MLDC introduces a hierarchical storage class (SC), in which every node is assigned to an SC with its own Access Frequency (AF) threshold based on node availability. To reduce the number of replications shared among participant nodes, each node in a SC continues to remove unaccessed data from local storage based on a threshold time determined by the AF threshold of the SC, while maintaining all block hashes for consistency. Eventually, all nodes in MLDC store the most frequently accessed data in their local storage, so MLDC effectively reduces the storage and query costs while minimising network overhead. We also analyse the security of MLDC and quantitatively evaluate its performance for both the uniform access and exponentially decaying access patterns. The evaluation was carried out on a representative blockchain simulator with 15 storage nodes. Our results from 11 hours of experiments producing 6667 blocks and 39997 transactions show good performance for MLDC. The results of the experimentation for the exponentially decaying assess pattern show that MLDC can reduce the total storage cost by 83% compared to conventional blockchain systems, while maintaining blockchain consistency and data availability with a slight increase in network overhead and query cost.

Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity. Most conventional blockchains rely on computationally expensive validator selection and consensus algorithms, have limited throughput, and high transaction delays. In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key. Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger.

Due to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. However, analysis of multiple security data could provide more comprehensive and system-wide anomaly detection in industrial networks. In this paper, we present an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. We validated our framework using two ICS datasets: a factory automation dataset and a Secure Water Treatment (SWaT) dataset. These datasets contain physical and network level normal and abnormal traffic. The performance of our new framework is compared with single-source machine learning methods. The precision of our framework is 95% which is comparable with single-source anomaly detectors. However, multi-source analysis is more robust because it can detect anomalies from multiple sources simultaneously, while achieving comparable precision for each of the sources.

Blockchain has received tremendous attention as a distributed platform to enhance the security of Internet of Things (IoT). The history of communications is stored in blockchain which introduces auditability. On the flip side, new privacy risks are introduced as the entire history of IoT device communication is exposed to participants. We study the likelihood of classifying IoT devices by analyzing the temporal patterns of their transactions, which to the best of our knowledge, is the first work of its kind. We apply machine learning algorithms on blockchain data to analyze the success rate of device classification. Our results demonstrate success rates over 90% in classifying devices. We propose three timestamp obfuscation methods, namely combining multiple packets into a single transaction, merging ledgers of multiple devices, and randomly delaying transactions, to reduce the success rate in classifying devices which reduce the classification success rates to as low as 24%.

Blockchain is a promising technology for establishing trust in IoT networks, where network nodes do not necessarily trust each other. Cryptographic hash links and distributed consensus mechanisms ensure that the data stored on an immutable blockchain can not be altered or deleted. However, blockchain mechanisms do not guarantee the trustworthiness of data at the origin. We propose a layered architecture for improving the end-to-end trust that can be applied to a diverse range of blockchain-based IoT applications. Our architecture evaluates the trustworthiness of sensor observations at the data layer and adapts block verification at the blockchain layer through the proposed data trust and gateway reputation modules. We present the performance evaluation of the data trust module using a simulated indoor target localization and the gateway reputation module using an end-to-end blockchain implementation, together with a qualitative security analysis for the architecture.

There is increased interest in smart vehicles acting as both data consumers and producers in smart cities. Vehicles can use smart city data for decision-making, such as dynamic routing based on traffic conditions. Moreover, the multitude of embedded sensors in vehicles can collectively produce a rich data set of the urban landscape that can be used to provide a range of services. Key to the success of this vision is a scalable and private architecture for trusted data sharing. This paper proposes a framework called SpeedyChain, that leverages blockchain technology to allow smart vehicles to share their data while maintaining privacy, integrity, resilience, and non-repudiation in a decentralized and tamper-resistant manner. Differently from traditional blockchain usage (e.g., Bitcoin and Ethereum), the proposed framework uses a blockchain design that decouples the data stored in the transactions from the block header, thus allowing fast addition of data to the blocks. Furthermore, an expiration time for each block is proposed to avoid large sized blocks. This paper also presents an evaluation of the proposed framework in a network emulator to demonstrate its benefits.