The inherent security and computational demands of classical consensus protocols are often presumed impervious to various forms of attack. However, quantum computing advancements present considerable threats to the assumed attack resistance of classical security strategies currently deployed within blockchain systems. Adopting consensus algorithms fortified with post-quantum security measures holds the potential to significantly enhance the privacy and security dimensions of traditional blockchains. Notable advantages of such post-quantum solutions in the context of blockchain consensus include accelerated transaction verification, clarified mining authorship, and resilience against quantum attacks. Yet, a comprehensive analysis of the implications of post-quantum solutions for blockchain consensus is notably absent in the existing scholarly discourse. This paper aims to bridge this gap by delivering a systematic review of Post-Quantum Blockchain Consensus (PQBC). The four primary contributions of this study are (i) a systematic approach to presenting a comprehensive overview of PQBC, (ii) the systematic selection and analysis of 29 key studies from an initial pool of 1192 papers, (iii) a critical review of methods, enhancements to security, scalability, trust, and privacy, as well as the evaluation employed for PQBC, and (iv) a discussion of primary gaps and prospective directions for future PQBC research.
Chryssis Georgiou, Michel Raynal, Elad M. Schiller
Numerous distributed applications, such as cloud computing and distributed ledgers, necessitate the system to invoke asynchronous consensus objects an unbounded number of times, where the completion of one consensus instance is followed by the invocation of another. With only a constant number of objects available, object reuse becomes vital. We investigate the challenge of object recycling in the presence of Byzantine processes, which can deviate from the algorithm code in any manner. Our solution must also be self-stabilizing, as it is a powerful notion of fault tolerance. Self-stabilizing systems can recover automatically after the occurrence of arbitrary transient faults, in addition to tolerating communication and (Byzantine or crash) process failures, provided the algorithm code remains intact. We provide a recycling mechanism for asynchronous objects that enables their reuse once their task has ended, and all non-faulty processes have retrieved the decided values. This mechanism relies on synchrony assumptions and builds on a new self-stabilizing Byzantine-tolerant synchronous multivalued consensus algorithm, along with a novel composition of existing techniques.
Because of the characteristics of decentralization, immutability, and transparency, blockchain has gradually become a new and revolutionary technology, which has far-reaching significance for the development of modern technology. However, the traditional Bitcoin blockchain that supports synchronous consensus suffers from the fatal flaw of low throughput. To improve throughput, a number of DAG distributed ledgers have been proposed that support asynchronous consensus, all of which allow multiple nodes to process concurrent transactions asynchronously. However, most DAG distributed ledgers do not implement consistent sorting of transactions, making it difficult to deploy smart contracts. To overcome this problem, in this paper, an epoch-based transaction consistency sorting protocol for DAG distributed ledger is proposed, which not only provides the possibility for the deployment of smart contracts but also can be used to resolve conflicting transactions in the ledger. Transaction consistency sorting protocol provides a more reasonably ordered list of all transactions by taking scalars, such as the set of their own past and future, parent block, and timestamp. In addition, through theoretical analysis, the stability and rationality of the transaction consistency sorting protocol are proved, and there is no Condorcet cycle. Finally, the simulation results demonstrate the protocol is efficient and achieve a throughput of at least 2000 transactions per second.
As a distributed ledger technology, the addition of new members in permissioned blockchain is usually composed of several steps among distributed nodes. The addition can not be considered successful until all of the steps are completed. In other words, these steps are an atomic operation. However, there is no solution for the atomic operation in existing permissioned blockchain, leading to an inconsistent state when the addition of new members is partially completed. To implement the atomic member addition in permissioned blockchain, we propose a method targeting at the atomic addition of new members based on distributed and autonomous rollback. After member addition starts, distributed nodes of existing members detect the new node and decide whether to rollback or not, instead of getting commands from the coordinator. After deciding to rollback, a new configuration block is added to achieve rollback of the uncompleted member addition. In order for the new configuration block to pass the policy validation of orderers, we set a rollback mode for orderers. The evaluation results show that our method can actually implement atomic member addition and has little impact on performance.
A rollup is a type of popular "layer two" scaling solution for slow-but-secure blockchains like Ethereum. A rollup perfoms computation of blockchain state updates off-chain but posts the inputs and the data to the underlying blockchain in order to benefit from its security. However, if rollup operators go offline, further state updates are no longer possible through the rollup; instead, state updates to the layer two state must be forced on the underlying blockchain. Such a mechanism is called an escape hatch as it allows state, and in particular digital assets, to escape from an inoperative rollup. We review the approaches from rollups developed by the community and highlight potential issues. We also establish a wish-list of properties that an escape hatch mechanism should have to be considered trustworthy and compatible with decentralization.
Neuromorphic intelligent systems are motivated by the observation that biological organisms - from algae to primates - excel in swiftly sensing their environment, reacting promptly to its perils and opportunities. Furthermore, biological organisms function more resiliently than our most advanced machines, with a fraction of their power requirements. Taking inspiration from how primates and humans have successfully evolved higher cognitive intelligence within social constructs, this paper proposes neuromorphic systems to be built and governed on a public distributed ledger platform. However, following in the footsteps of generic AI research, neuromorphic benchmarks and algorithms are developed in isolation. Furthermore, as a relatively niche research field, there is limited access to the actual neuromorphic sensors and large publicly available curated data, exacerbating the slow research progress. Nonetheless, centralized neuromorphic datasets and algorithms pose a threat to secure closed-loop behavior and learning outcomes, both commonly modulated in biological organisms via social interactions. This paper makes the case for early adoption of distributed ledger technology by neuromorphic systems and benchmarks to avoid the pitfalls endured by AI research - showcasing competing event-based gesture recognition systems on the Ethereum smart contract platform. This shift towards real-world and dynamic systems on a distributed ledger platform will improve collaboration among neuromorphic researchers while enabling healthy competition via incentives. Smart contract protocols allow model behavior monitoring, setting new learning tasks and increase in baseline performance, and naturally provides a governance framework for evolving neuromorphic systems. The code is publicly made available at: https://ist.github.com/BruceFan123.
The concept of verifiable delay functions has received attention from researchers since it was first proposed in 2018. The applications of verifiable delay are also widespread in blockchain research, such as: computational timestamping, public random beacons, resource-efficient blockchains, and proofs of data replication. This paper introduces the concept of verifiable delay functions and systematically summarizes the types of verifiable delay functions. Firstly, the description and characteristics of verifiable delay functions are given, and weak verifiable delay functions, incremental verifiable delay functions, decodable verifiable delay functions, and trapdoor verifiable delay functions are introduced respectively. The construction of verifiable delay functions generally relies on two security assumptions: algebraic assumption or structural assumption. Then, the security assumptions of two different verifiable delay functions are described based on cryptography theory. Secondly, a post-quantum verifiable delay function based on super-singular isogeny is introduced. Finally, the paper summarizes the blockchain-related applications of verifiable delay functions.
Shan Wang, Ming Yang, Bryan Pearson, Tingjian Ge · 6 authors
Attacks on consensus protocols against a blockchain system are often caused by inner malicious nodes, and inject valid but malicious transactions or blocks to the blockchain by exploiting the consensus protocol. Much attention is paid to attacks such as the 51% attack on the Proof-of-Work (PoW) and long range attack on Proof-of-Stake (PoS) on the consensus protocol in a public blockchain, where the attack cost is high. There is no much systematic work on the attacks on the consensus protocol in a permissioned blockchain. In this paper, we perform a holistic security study of the “execute-order-validate” paradigm used by a permissioned blockchain system such as Hyperledger Fabric. We first systematically present the consensus protocol in the execute-order-validate blockchain paradigm and abstract the consensus protocol as Proof-of-Policy (PoP). We then analyze the chaincode deployment process of Fabric and show it can be exploited to deploy malicious chaincode to launch collusion attacks against PoP. The collusion attacks do not incur high computational cost or monetary cost like attacks on PoW and PoS. The scale of a permissioned blockchain system is often limited, and there is no built-in penalty for such attacks. Therefore, the risk of those collusion attacks is high compared with those against public blockchain systems. We build a Fabric test network to validate the attacks. A large-scale analysis is performed on 7036 Fabric projects on GitHub to evaluate the attack generality.
Vikas Rao Vadi, Shafiqul Abidin, Azimuddin Khan, Mohd Izhar
Abstract In general, due to the complexity and limited computation capabilities, the security issues occur in Internet of Things (IoT). Security protocols are required to increase the security of the system. Therefore, in this article, an enhanced Elman spike neural network (EESNN) with green proof of work consensus algorithm (GPoW) is proposed for enhancing the security of IoT network. Initially, the generalized security mechanism as EESNN approach is proposed for the IoT network by categorizing the devices into malicious and benign. Then, the GPoW consensus algorithm is used for enhancing the security of the devices from malicious attacks. Subsequently, a coalition formation (CF) algorithm is used for reducing the excess energy consumption in a network. The proposed EESNN‐GPoW‐CF approach has effectively classified the malicious attacks and enhances the security of the IoT network. The simulation of this work is done in Python. From the simulation, the proposed EESNN‐GPoW‐CF approach attains high efficiency outcomes in terms of accuracy, recall, precision, PDR, PLR, throughput, overhead, computation time, and delay. Moreover, the proposed EESNN‐GPoW‐CF approach attains 3.1%, 5.3%, 7.4% high accuracy rate, and 7.5%, 12.5%, 14.7% lower computation time with 4.8%, 2.3%, 5.7% lower energy utilization than the existing methods, such as deep learning based blockchain for IoT security, deep reinforcement learning based blockchain for IoT security, and deep blockchain‐based trustworthy privacy preserving secured framework in IoT, respectively.
To protect participants’ confidentiality, blockchains can be outfitted with anonymization methods. Observations of the underlying network traffic can identify the author of a transaction request, although these mechanisms often only consider the abstraction layer of blockchains. Previous systems either give topological confidentiality that may be compromised by an attacker in control of a large number of nodes, or provide strong cryptographic confidentiality but are so inefficient as to be practically unusable. In addition, there is no flexible mechanism to swap confidentiality for efficiency in order to accommodate practical demands. We propose a novel approach, the neural fairness protocol, which is a blockchain-based distributed ledger secured using neural networks and machine learning algorithms, enabling permissionless participation in the process of transition validation while concurrently providing strong assurance about the correct functioning of the entire network. Using cryptography and a custom implementation of elliptic curves, the protocol is designed to ensure the confidentiality of each transaction phase and peer-to-peer data exchange.
Konstantinos Sgantzos, Ian Grigg, Mohamed Al Hemairy
Most Artificial Intelligence (AI) implementations so far are based on the exploration of how the human brain is designed. Nevertheless, while significant progress is shown on specialized tasks, creating an Artificial General Intelligence (AGI) remains elusive. This manuscript proposes that instead of asking how the brain is constructed, the main question should be how it was evolved. Since neurons can be understood as intelligent agents, intelligence can be thought of as a construct of multiple agents working and evolving together as a society, within a long-term memory and evolution context. More concretely, we suggest placing Multiple Neighborhood Cellular Automata (MNCA) on a blockchain with an interaction protocol and incentives to create an AGI. Given that such a model could become a “strong” AI, we present the conjecture that this infrastructure is possible to simulate the properties of cognition as an emergent phenomenon.
Blockchain has gained considerable traction over the last few years and plays a critical role in realizing decentralized and cryptocurrency applications. A challenge that has been over-looked in prior blockchain algorithms is that they do not consider large-scale network outages and relied on the assumption of a reliable global network connectivity. In the event of a large scale network partition, forks may occur between partitioned regions. After the partition ends they will be discarded, leading to the loss of many blocks and a considerable amount of wasted work. This paper presents PeloPartition, which provides a sharding mechanism to improve blockchain's resilience to the possibility of a global internet outage. In PeloPartition we form consensus groups dynamically and consider the partitioning of the group as a hint to split the blockchain into branches and guarantee that all of them will be merged after the network is recovered. We indicate different methodologies to ensure blockchain security while partitioning occurs. Our experiments use simulations to show how this approach can improve the performance of blockchain algorithms and prevent wasted computational power during partitioning.
Purpose Sharding of blockchains consists of partitioning a blockchain network into several sub-networks called “shards,” each shard processing and storing disjoint sets of transactions in parallel. Sharding has recently been applied to public blockchains to improve scalability through parallelism. The throughput of sharded blockchain is optimized when the workload among the shards is approximately the same. The purpose of this paper is to investigate the problem of balancing workload of account-based blockchains such as Ethereum. Design/methodology/approach Two known consensus-based distributed load-balancing algorithms have been adapted to sharded blockchains. These algorithms migrate accounts across shards to balance transaction processing times. Two methods to predict transaction processing times are proposed. Findings The authors identify some challenging aspects for solving the load-balancing problem in sharded blockchains. Experiments conducted with Ethereum transactions show that the two load-balancing algorithms are challenged by accounts often created to process a single transaction to optimize anonymity, while existing accounts sparsely generate transactions. Originality/value Tests in this work have been conducted on transactions originating from a blockchain platform rather than using artificially generated data distributions. They show the specificity of the load-balancing problem for sharded blockchains, which were hidden in artificial data sets.
Venkata K. V. V. Bathalapalli, Saraju P. Mohanty, Elias Kougianos, Babu Kaji Baniya · 5 authors
This article presents the first-ever hardware-assisted blockchain for simultaneously handling device and data security in smart healthcare. This article presents the hardware security primitive physical unclonable functions (PUF) and blockchain technology together as PUFchain 2.0 with a two-level authentication mechanism. The proposed PUFchain 2.0 security primitive presents a scalable approach by allowing Internet of Medical Things (IoMT) devices to connect and obtain PUF keys from the edge server with an embedded PUF module instead of connecting a PUF module to each device. The PUF key, once assigned to a particular media access control (MAC) address by the miner, will be unique for that MAC address and cannot be assigned to other devices. PUFs are developed based on internal micro-manufacturing process variations during chip fabrication. This property of PUFs is integrated with blockchain by including the PUF key of the IoMT into blockchain for authentication. The robustness of the proposed Proof of PUF-Enabled authentication consensus mechanism in PUFchain 2.0 has been substantiated through test bed evaluation. Arbiter PUFs have been used for the experimental validation of PUFchain 2.0. From the obtained 200 PUF keys, 75% are reliable and the Hamming distance of the PUF module is 48%. Obtained database outputs along with other metrics have been presented for validating the potential of PUFchain 2.0 in smart healthcare.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Industrial Internet-of-Things (IIoT) is a powerful IoT application which remodels the growth of industries by ensuring transparent communication among various entities such as hubs, manufacturing places and packaging units. Introducing data science techniques within the IIoT improves the ability to analyze the collected data in a more efficient manner, which current IIoT architectures lack due to their distributed nature. From a security perspective, network anomalies/attackers pose high security risk in IIoT. In this paper, we have addressed this problem, where a coordinator IoT device is elected to compute the trust of IoT devices to prevent the malicious devices to be part of network. Further, the transparency of the data is ensured by integrating a blockchain-based data model. The performance of the proposed framework is validated extensively and rigorously via MATLAB against various security metrics such as attack strength, message alteration, and probability of false authentication. The simulation results suggest that the proposed solution increases IIoT network security by efficiently detecting malicious attacks in the network.
Network function virtualization is a key enabling technology in future wireless networks for flexible and efficient sharing of network resources. Due to the increasing heterogeneity of network resource providers, a blockchain-based distributed architecture is a promising solution to enable reliable and transparent virtualized network function (VNF) management. However, since on-chain storage and computation are costive, it becomes a challenging task to achieve efficient VNF management with blockchain. In this paper, we first introduce a consortium blockchain for collaborative VNF management among network resource providers. Then, we propose an authenticated VNF dictionary that can be stored as a succinct authenticator on blockchain to support rich VNF query functionalities and efficient verifications of query results. Moreover, we design a dictionary pruning strategy to securely generate a compact authenticator for a given query, which reduces unnecessary memory accesses of the original dictionary when VNF queries are represented as arithmetic circuits. Finally, we conduct extensive experiments with a consortium blockchain network. The experimental results demonstrate that our pruning strategy is efficient for both on-chain and off-chain VNF management.
Tao Li, Yaozheng Fang, Ye Lu, Jinni Yang · 7 authors
Blockchain-based artificial intelligence (BC-AI) has been applied for protecting deep neural network (DNN) data from being tampered with, which is expected to further boost trusted distributed AI applications in many fields. However, due to smart contract execution environment architectural defects, it is challenging for previous BC-AI systems to support computing-intensive tasks on-chain performing such as DNN convolution operations. They have to offload computations and a large amount of data from blockchain to off-chain platforms to execute smart contracts as native code. This failure to take advantage of data locality has become one of the major critical performance bottlenecks in BC-AI system. To this end, in this article, we propose SmartVM with optimization methods to support on-chain DNN inference for BC-AI system. The key idea is to design and optimize the computing mechanism and storage structure of smart contract execution environment according to the characteristics of DNN such as high computational parallelism and large data volume. We decompose SmartVM into three components: 1) a compact DNN-oriented instruction set to describe computations in a short number of instructions to reduce interpretation time. 2) a memory management mechanism to make SmartVM memory dynamic free/allocated according to the size of DNN feature maps. 3) a block-based weight prefetching and parallel computing method to organize each layer's computing and weights prefetching in a pipelined manner. We perform the typical image classification in a private Ethereum blockchain testbed to evaluate SmartVM performance. Experimental results highlight that SmartVM can support DNN inference on-chain with roughly the same efficiency against the native code execution. Compared with the traditional off-chain computing, SmartVM can speed up the overall execution by70×,16×,11×, and12×over LeNet5, AlexNet, ResNet18, and MobileNet, respectively. The memory footprint can be reduced by84%,90.8%,94.3%, and93.7%over the above four models, while offering the same level model accuracy. This article sheds light on the design space of the smart contract virtual machine for DNN computation and is promising to further boost BC-AI applications.