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Since 2011, the private ride-hailing companies Didi (2019), Uber (2019) and Lyft (2021) have expanded into more and more cities. These ride-hailing services (RHS) bring convenience to our life; however, at the same time they, also raise security concerns for users. For example, several recent news items show that a considerable number of registered drivers whose licenses have been revoked are still taking RHS orders on the respective platforms; this phenomenon directly leads to insecurity on part of its users and the bad reputation of the ride-hailing service provider (SP). The traditional solution to solve this problem is to periodically check the validity of the drivers' licenses; however, it is a considerably time-consuming and costly process since the SPs have to manually interact with the governing authorities. Therefore, in this paper, we have presented an auditable self-sovereign identity system (named AudiSSI), which provides an efficient approach for the SPs to manage their registered drivers' qualifications in a decentralized and automatic manner. Further, using smart contract technology, we propose a safety guarantee insurance in the form of an auditing contract to enable the RHS rider to check their driver's qualifications before the trip starts and get incentives once they detect a disqualified driver. We designed an incentive mechanism and have provided a game theoretical analysis. Finally, we implemented a prototype of AudiSSI and deployed it on Hyperledger Indy and Fabric to show that self-sovereign identity system for RHS driver with qualification auditing is efficient and technically feasible.

Blockchain has become one of the most developing technologies in our country. Blockchain technology is a decentralized, distributed ledger that records the sources of digital assets. Electronic Medical Record (EMR) accommodates patient’s medical history, which has been carried out by a Physician or Doctor in a particular clinic or hospital or Healthcare center. Whereas, Electronic Health Record (EHR) holds patient’s entire health history, including immunization plans, diagnosis, lab reports, inoculation schedule, etc.,. This feature empowers the usage of Health IT in a more secure way with the amendment of blockchain technology. These records are maintained smart contract between the patient and the healthcare sector. In both EMR and EHR, the patient is the owner of his own data. This paper incorporates the features of blockchain technology to provide a collaborative work on Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) and it concentrates highly in diabetes as it has a sharp magnification in current world. Though it is tedious to implement this EHR all over the country, this paper works for its implementation especially in the domain of diabetes.

Smart contracts are a vital component of applications being built by blockchain or distributed ledger technology. Smart contracts consists of computer code that composed set of rules agreed upon by the involved parties. When these predefined conditions or rules are satisfied by the transactions initiated by parties, the smart contract executes itself to complete the transaction. Normally, while performing transactions with agreements involving multiple parties, a third party have to be involve to verify all the information, which makes it complex and time consuming process. Smart contracts simplify this process by eliminating the third party and automating the process, enabling the stakeholders to perform transactions directly with each other. Smart contract itself is replicated among multiple nodes of a blockchain there by giving benefits of immutability, security and permanence. Most smart contracts are written in Solidity programming language due to its simplicity and conciseness. Attackers are attracted by the popularity of Solidity language and its vulnerability possibilities. For example, in the 2016 year, 60 million US dollars was theft by Decentralized Autonomous Organization (DAO) attack due to vulnerabilities present in smart contracts. There are few existing tools and papers on this area to find vulnerabilities on smart contracts but taking more time to predict. Thus, this paper presents different Deep learning techniques with satisfactory results to identify smart contract vulnerabilities which are Re-entrancy, Denial of Service (DOS) and Transaction Origin using binary, multi class and multi label classification techniques.

Healthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration in a decentralized open healthcare network environment. Although the healthcare blockchain technology has attracted broad interests and attention in industry, government and academia, the security and privacy concerns remain the focus of debate when deploying blockchains for information sharing in the healthcare sector from business operation to research collaboration. This paper focuses on the security and privacy requirements for medical data sharing using blockchain, and provides a comprehensive analysis of the security and privacy risks and requirements, accompanied by technical solution techniques and strategies. First, we discuss the security and privacy requirements and attributes required for electronic medical data sharing by deploying the healthcare blockchain. Second, we categorize existing efforts into three reference blockchain usage scenarios for electronic medical data sharing, and discuss the technologies for implementing these security and privacy properties in the three categories of usage scenarios for healthcare blockchain, such as anonymous signatures, attribute-based encryption, zero-knowledge proofs, verification techniques for smart contract security. Finally, we discuss other potential blockchain application scenarios in healthcare sector. We conjecture that this survey will help healthcare professionals, decision makers, and healthcare service developers to gain technical and intuitive insights into the security and privacy of healthcare blockchains in terms of concepts, risks, requirements, development and deployment technologies and systems.

This paper proposes SilentDelivery, a secure, scalable and cost-efficient protocol for implementing timed information delivery service in a decentralized blockchain network. SilentDelivery employs a novel combination of threshold secret sharing and decentralized smart contracts. The protocol maintains shares of the decryption key of the private information of an information sender using a group of mailmen recruited in a blockchain network before the specified future time-frame and restores the information to the information recipient at the required time-frame. To tackle the key challenges that limit the security and scalability of the protocol, SilentDelivery incorporates two novel countermeasure strategies. The first strategy, namely silent recruitment, enables a mailman to get recruited by a sender silently without the knowledge of any third party. The second strategy, namely dual-mode execution, makes the protocol run in a lightweight mode by default, where the cost of running smart contracts is significantly reduced. We rigorously analyze the security of SilentDelivery and implement the protocol over the Ethereum official test network. The results demonstrate that SilentDelivery is more secure and scalable compared to the state of the art and reduces the cost of running smart contracts by 85%., This paper has been accepted by IEEE Transactions on Services Computing

The rapid growth of storage overhead on public blockchains has urged the use of light clients that only store a small fraction of blockchain data and rely on other bootstrapped full nodes for data retrievals. Unfortunately, current blockchain light client designs are far from satisfactory. Firstly, outsourcing retrieval requests could raise severe concerns about result correctness and privacy threats. Secondly, current light clients do not support rich query and enforce fee payments to full nodes. Given that blockchain storage increases day by day, enabling effective rich blockchain queries and fairly compensating full nodes ever growing costs has become extremely necessary. In this paper, we propose a general and secure paid query framework to simultaneously meet those demands above. Specifically, we leverage the integration of trusted hardware (e.g., Intel SGX) and smart contract as a starting point for building efficient yet secure query processing with fair payments. Then, we further craft several crucial performance and security refinement designs to boost query efficiency and enforce result correctness, and also explore an enclave-facilitated fair settlement mechanism for on-chain cost optimizations. We implement a prototype of our paid query framework and the experimental result has demonstrated its practically affordable cost.

Ongoing smart contract attack events have seriously impeded the practical application of blockchain. Although lots of researches have been conducted, they mostly focus on off-chain vulnerability detection. However, smart contract cannot be modified once they have been deployed on chain, and thus existing techniques cannot protect those deployed contracts from being attacked. To mitigate this problem, we propose Aroc, a general repairer that can automatically patch vulnerable deployed smart contracts. The core insight of Aroc is to generate a patch contract leveraging static analysis techniques to verify whether transactions obey secure states of the vulnerable contracts, and then abort those deviated transactions in advance. Take the three most serious bug types (i.e., reentrancy, arithmetic bugs, and unchecked low-level checks) as examples, we present how Aroc is able to automatically repairs them on chain. Experimental results show that Aroc can automatically repair 84.95% of the vulnerable contracts with an average correctness ratio of 91.43%. Meanwhile, Aroc introduces acceptable additional overheads to smart contract users and blockchain miners.

Although the emergence of the programmable smart contract makes blockchain systems easily embrace a wide range of industrial services, how to execute smart contracts efficiently becomes a big challenge nowadays. Due to the existence of Byzantine nodes, existing mature concurrency control protocols in database cannot be employed directly, since the mechanism of executing smart contracts varies a lot. Furthermore, even though smart contract execution follows a two-phase style, i.e., the primary node executes a batch of smart contracts in the first phase and the validators replay them in the second phase, existing parallel solutions merely focus on the optimization for the first phase, rather than the second phase. In this paper, we propose a novel two-phase concurrency control protocol to optimize both phases for the first time. First, the primary executes transactions in parallel and generates a transaction dependency graph with high parallelism for validators. Then, a graph partition algorithm is devised to divide the original graph into several sub-graphs to preserve parallelism and reduce communication cost remarkably. Finally, we propose a deterministic replay protocol to re-execute the primary's parallel schedule concurrently. Moreover, this two-phase protocol is further optimized by integrating with PBFT. Theoretical analysis and extensive experimental results illustrate that the proposed scheme outperforms state-of-art solutions significantly.

Recently, decentralization has been extensively explored by researchers. Blockchain, as a representation of decentralized technology, has attracted attention with its unique characteristics, such as irrevocability and security. Consequently, herein, we introduce cutting-edge blockchain technologies from four directions: blockchain system, consensus algorithms, smart contract, and scalability. Subsequently, we analyze the current lack of consensus mechanism, fault tolerance, and block capacity of the blockchain, and the integration of blockchain into 5G/6G mobile communication. Furthermore, we discuss the possible applications of blockchain in intellectual property protection, the Internet of Things, digital twins, standardization, and epidemic prevention and control. Finally, explore the impacts and solutions of blockchain on human society beyond technology.

Network storage services have benefited countless users worldwide due to the notable features of convenience, economy and high availability. Since a single service provider is not always reliable enough, more complex multi-cloud storage systems are developed for mitigating the data corruption risk. While a data auditing scheme is still needed in multi-cloud storage to help users confirm the integrity of their outsourced data. Unfortunately, most of the corresponding schemes rely on trusted institutions such as the centralized third-party auditor (TPA) and the cloud service organizer, and it is difficult to identify malicious service providers after service disputes. Therefore, we present a blockchain-based multi-cloud storage data auditing scheme to protect data integrity and accurately arbitrate service disputes. We not only introduce the blockchain to record the interactions among users, service providers, and organizers in data auditing process as evidence, but also employ the smart contract to detect service dispute, so as to enforce the untrusted organizer to honestly identify malicious service providers. We also use the blockchain network and homomorphic verifiable tags to achieve the low-cost batch verification without TPA. Theoretical analyses and experiments reveal that the scheme is effective in multi-cloud environments and the cost is acceptable.

As a promising alternative solution to cloud storage, decentralized storage networks (DSN) are widely anticipated to develop continuously and reshape the storage market share in the foreseeable future. In particular, one of the most important research problems is how to enforce the quality of service (QoS) in the context of storage solutions. Despite plenty of auditing-related works in the context of cloud storage, none of them can be directly applied to the decentralized storage paradigm. The challenges of designing a feasible storage auditing framework emanate from two aspects: 1) security problems unique to the decentralized settings; 2) performance overhead due to on-chain operations. In this work, we first put forward a basic storage auditing framework that satisfies the security and efficiency requirements, and outperforms the existing approaches. We also identify a critical and overlooked security problem that would compromise the integrity of storage auditing solutions in the blockchain paradigm. With our refined storage auditing design based on customized zero knowledge protocols, we propose a convenient mitigation solution in our revised security model. The evaluation results confirm that our solution would only incur a 10%-15% increase in the overall auditing costs for common usage scenarios, compared to the basic design.

Cooperative Intelligent Transport System (C-ITS) is a promising technology that aims to improve the traditional transport management systems. In C-ITS infrastructure Autonomous Vehicles (AVs) communicate wirelessly with other AVs, Road Side Units (RSUs) and Traffic Command Centres (TCCs) using an open channel Internet. However, the use of the Internet brings inherent vulnerabilities related to privacy (e.g., adversary performing inference and data poisoning attacks), and security (e.g., AVs can be compromised using advanced hacking techniques) issues and prevents the faster realization of C-ITS applications. To address these challenges, this paper presents a privacy-preserving-based secure framework to provide both privacy and security in C-ITS infrastructure. The proposed framework provides two level of security and privacy using blockchain and deep learning modules. First, a blockchain module is designed to securely transmit the C-ITS data between AVs-RSUs-TCCs, and a smart contract-based enhanced Proof of Work (ePoW) technique is designed to verify data integrity and mitigate data poisoning attacks. Second, a deep-learning module is designed that includes Long-Short Term Memory-AutoEncoder (LSTM-AE) technique for encoding C-ITS data into a new format to prevent inference attacks. The encoded data is used by the proposed Attention-based Recurrent Neural Network (A-RNN), for intrusive events recognition in C-ITS infrastructure. The proposed A-RNN is trained using Truncated Backpropagation Through Time (BPTT) algorithm. The framework is further validated and tested using two publicly available ToN-IoT and CICIDS-2017 datasets. The proposed framework is compared with peer privacy-preserving intrusion detection techniques, and the result shows the effectiveness of the proposed framework over several state-of-the-art techniques in both blockchain and non-blockchain systems.

With the advent of mobile crowdsensing, the mobile devices equipped with a variety of sensors (such as accelerometer, gyroscope, microphone etc.) are used to collect sensory data. A data aggregator processes the collected sensor data to deliver various services such as traffic management, health care and environmental monitoring. To ensure the privacy of the data, privacy-preserving aggregation (PPA) has attracted much attention since it can find aggregated statistics on the encrypted data. In this paper, we extend the existing PPA schemes in two directions: (1) Aggregator unforgeability - The aggregator performs the aggregation operation correctly. Although there are some schemes which consider aggregator unforgeability, they rely on cryptographic techniques. (2) Fair payments - The data owners receive the payments for their data contribution if and only if the aggregator receives the data. Contrary to existing works, we achieve the aggregator unforgeability and fair payments by modeling the aggregator as a smart contract running on a public Blockchain network. We design two PPA schemes FairNaivePPA and FairPPA for secure aggregation of MCS data with fair payments. We show the financial and transactional cost analysis of proposed contracts by implementing them in solidity and running them on Ethereum Blockchain.

Today's blockchains suffer from low throughput and high latency, which impedes their widespread adoption of more complex applications like smart contracts. In this paper, we propose a novel paradigm for smart contract execution. It distinguishes between consensus nodes and execution nodes: different groups of execution nodes can execute transactions in parallel; meanwhile, consensus nodes can asynchronously order transactions and process execution results. Moreover, it requires no coordination among execution nodes and can effectively prevent livelocks. We show two ways of applying this paradigm to blockchains. First, we show how we can make Ethereum support parallel and asynchronous contract execution \emph{without hard-forks}. Then, we propose a new public, permissionless blockchain. Our benchmark shows that, with a fast consensus layer, it can provide a high throughput even for complex transactions like Cryptokitties gene mixing. It can also protect simple transactions from being starved by complex transactions.

Unlike a traditional centralized and producer-controlled power grid, the smart grid is a more complicated distributed power system consisted of many resources and applications. In smart grid, huge amounts of data generated by edge devices are collected by different parties. To achieve high operation efficiency, it is important to enable the data sharing and cooperative computation among different parties. How to protect the security and privacy of the utility data and the identities of their owners has become a major concern. There have been some studies on this issue. However, most of these works failed to consider the privacy protection in the dual sides of the data owner and receiver. In this paper, we propose BPM4SG, a Blockchain-based dual-side Privacy-preserving Multi-party computation scheme for edge-enabled Smart Grid. In BPM4SG, the data segmentation method is adopted to ensure the security of multi-party computation (e.g., summation) in edge nodes. The consortium blockchain and smart contract are used to further increase the system security and avoid the dependency on trusted third parties. Additionally, a data obfuscation method based on the ring signatures and a new one-time address scheme are proposed to protect the privacy of both the data owner and data receiver. The analysis shows that BPM4SG can meet the security and privacy requirements of smart grid. The experimental evaluation results demonstrate that our scheme has a better performance compared with other popular schemes.

With the emergence of computation intensive vehicular applications, computation offloading based on mobile edge computing (MEC) has become a promising paradigm in resource constrained vehicular cloud networks (VCNs). However, when doing computation offloading in a VCN, malicious service providers can cause serious security concerns on the content offloading. To address that, in this paper a blockchain based secure computation offloading scheduling scheme is proposed. It embraces the blockchain based trust management paradigm and smart contract enabled Deep Reinforcement Learning (DRL) algorithm. As for the trust management, the long-term reputation and short-term trust variability are jointly considered. Specifically, a novel three-valued subjective logic (3VSL) scheme is adopted to obtain a more comprehensive reputation, and the statistics of behavioral transitions can provide a short-term trust variability to timely capture the malicious behaviors. In addition, to securely update, validate, and store the trust information, we propose a hierarchical blockchain framework that comprises vehicular blockchain, RSU blockchain, and cloud blockchain. Furthermore, a smart contract enabled DRL algorithm is proposed to implement the secure and intelligent computation offloading scheduling in a VCN. Simulations are conducted to verify the effectiveness of the proposed scheme.

The Industrial Internet of Things (IIoT) which introduces Internet of Things (IoT) technology into industrial environments is beneficial to construct smart factories. It utilizes various sensors to collect the data of industrial devices. Those data will be analysed to improve manufacturing efficiency and product quality. Cloud storage provides a solution for storing data outsourced, especially for sensors that have limited local storage and computational capacity. To ensure the privacy preserving of devices, the collected data should be stored in the formal of ciphertext. Therefore, encrypted data sharing should be implemented to analyse the devices data. In this paper, the cloud storage solution for sensors is considered. To achieve secure and efficient data storage and sharing, a novel group signature scheme, which has less computation overhead and communication overhead, is designed to realize anonymous authentication firstly. And then, a novel blockchain-based cloud storage protocol for sensors in IIoT is constructed on basis of the proposed group signature scheme. Smart contract and proxy re-encryption are utilized in this protocol to realize secure data sharing with less computational overhead. Furthermore, security proofs and performance evaluations demonstrate that this protocol is secure, privacy-preserving, and has at least 40% and 20% performance improvement in data storage and sharing phase, respectively.

With the fast development of the electric vehicle (EV) technology, EVs are expected to be the mainstream in future. The large number of EVs facilitate development of the emerging vehicle-to-grid (V2G) technology, which realizes two- way electricity flows between EVs and the power grid. How to achieve fairness and privacy for EVs during electricity/service exchanges remains a challenging problem for V2G. In this paper, we propose a privacy-preserving fair exchange scheme V2GEx for V2G based on the blockchain. V2GEx is composed of an extended blockchain that supports zero-knowledge funds, a fair exchange smart contract based on the hashchain micropayment mechanism, and a privacy-preserving protocol for V2G. We further propose a simpler and more efficient scheme called Uni-V2GEx, which preserves privacy for only one party. We also provide a rigorous security proof under the universal composability (UC) model to prove V2GEx's security. To evaluate its efficiency, we implement V2GEx and conduct comprehensive experiments to test its performance in terms of computation cost and processing delay. The experiment results show that V2GEx is highly efficient in that verification of V2GEx transactions cost only 20 ms and the average transaction processing latency is around 6 seconds in a 200-node blockchain network.

To support the increasing computation-intensive applications in the Internet of Things (IoT), edge computing is introduced to provide mobile devices computing resources for performing low-latency tasks. Therefore, how to design an effective and secure computing resource allocation mechanism is attracting increasing attention. A lot of works have been done to design an effective computational resource market for IoT, but the problems of vulnerability and inefficiency still exist. In this paper, we propose a two-level Stackelberg game-based computing resource trading mechanism for mobile IoT devices with a credit-based payment approach, which is implemented by smart contracts on blockchain. In our model, the Stackelberg game consists of two levels, i.e., leader-level and user-level. In the leader-level, the computing service provider (CSP) and its agent constitute a composite leader. The agent purchases computing resource from CSP on credit and acts as a broker among leader-level and user-level reselling these computing resources to users. In the user-level, every user experiences social externality, which means users are interdependent. The leader-level subgame makes credit payment easier by making loaning and trading become a joint credit payment. The user-level subgame makes the market more active and closer to reality by introducing social externality. Besides, smart contracts can prevent malicious behaviors such as delay payment. We also conduct equilibrium analysis and prove the existence and uniqueness of the Nash equilibrium in our Stackelberg game-based model. Finally, we conduct numerical experiments to evaluate the cost of smart contracts and the performance of each entity with the proposed pricing mechanism.

The security of HTTPS fundamentally relies on SSL/TLS certificates issued by Certificate Authorities (CAs), which, however, are vulnerable to be compromised to issue unauthorized certificates (i.e., certificates issued without domains’ permission). Current countermeasures such as Certificate Transparency (CT) can only detect unauthorized certificates rather than preventing them. In this article, we present Pistis , a framework for issuing authorized and trusted certificates with the distributed ledger and Trusted Execution Environment (TEE) technology. In Pistis , TEE nodes validate whether the domain in a requested certificate passes the domain ownership validation (i.e., under corresponding applicants’ control) and submit attested results to a smart contract in the distributed ledger. The smart contract issues a certificate to the applicant when an attested result shows a pass. Therefore, Pistis can ensure its issued certificates are authorized due to the domain ownership validation mechanism in the TEE. Furthermore, as the issued certificates are stored in a Merkle Patricia Tree (MPT) in Pistis , they are trusted and can be verified by a normal user easily. The security of Pistis is formally proved in the Universally Composable (UC) framework. Compared with state-of-the-art, Pistis avoids potential damages by preventing unauthorized certificates from issuing.

Crowdsourcing has attracted widespread attention in recent years and developed into various applications. An indispensable service of crowdsourcing systems is task recommendation, which means tasks should be accurately recommended to the workers with aligned interests. However, existing systems rely on their separate servers to conduct recommendation services, resulting in computing resources locked inside each isolated system. Moreover, due to the wide attacking surfaces of traditional centralized servers setting, existing systems are subject to single points of failure or malicious data breaches. Therefore, failure to address these inherent limitations properly will hinder the wide adoption of crowdsourcing. In this paper, we propose and implement FedCrowd, the first federated and privacy-preserving crowdsourcing platform by using blockchain technology. Our main idea is to employ the smart contract as a trusted platform for systems to release encrypted tasks, and carefully craft matching protocols to enable efficient task recommendations in the ciphertext domain. Our task-matching protocols are highly customized for the decentralized settings, where users can securely perform keyword and range-based queries over federated task indexes without sharing secret keys. We formally analyze the security strengths and complete the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of the FedCrowd platform.

Blockchain, an emerging paradigm of secure and shareable computing, is a systematic integration of 1) chain structure for data verification and storage, 2) distributed consensus algorithms for generating and updating data, 3) cryptographic techniques for guaranteeing data transmission and access security, and 4) automated smart contracts for data programming and operations. However, the progress and promotion of Blockchain have been seriously impeded by various security issues in blockchain-based applications. Furthermore, previous research on blockchain security has been mostly technical, overlooking considerable business, organizational, and operational issues. To address this research gap from the perspective of information systems, we review blockchain security research in three levels, namely, the process level, the data level, and the infrastructure level, which we refer to as the PDI model of blockchain security. In this survey study, we first examine the state of blockchain security in the literature. Based on the insights obtained from this initial analysis, we then suggest future directions of research in blockchain security, shedding light on urgent business and industrial concerns in related computing disciplines.

With the increase of participants data and the number of smart meters in smart grid, the efficiency and privacy protection of electricity trading are remaining challenging. To reduce the cost and latency of electricity trading, we design a new architecture for smart meters that can support in-stu transactions by blockchain wallet and initiate transmission switch instructions by smart contacts. At the same time, we design a blockchain-based anonymous authentication scheme for fast and privacy-aware roaming to guarantee the protection of privacy. Besides, some potential attacks may occurred since the existence of untrustworthy third parties, thus we propose a decentralized P2P electricity trading scheme that can enable automatically electricity transmission by smart contract instead of centralized parties. Finally, we analyze our scheme according to the UC (Universally Composable) security framework and propose some industrial applications, which demonstrate the security, efficiency and feasibility of our scheme.

Blockchain-based Internet of Things (BC-IoT) brings the advantages of blockchain into traditional IoT systems. In BC-IoT, the smart contract has been widely used for automatic, trusted and decentralized applications. Smart contracts require frequently adjust and fast update due to various reasons such as inevitable code bugs, changes of applications or security requirements. However, previous smart contract architecture and updating mechanism are low speed and cause high overhead, because they are based on recompilation and redeployment in BC-IoT. Meanwhile, smart contract execution is so timeconsuming due to contract instruction dispatching and operand loading in the stack-based Ethereum Virtual Machine (EVM). To address these issues, we propose a new smart contract architecture and optimization mechanism for blockchain-based Internet of Things, ATOM, which provides architectural supports to update contract economically and fast executing in instructionwise for the first time, to the best of our knowledge. We design a compact Application-oriented Instruction (AoI) set to describe application operations. We can construct the bytecode of smart contract from application by directly assembling templates prebuilt upon the AoIs rather than by compilation. We also present an optimized mechanism for AoI execution to enable access addressable storage place rather than the indirect access through stack. We perform ATOM on a BC-IoT testbed based on private Ethereum and Hyperledger Burrow. The experimental results highlight that, ATOM is more efficient than state-of-the-art approaches. ATOM can reduce update latency by 62.7%, ledger size by 70% and gas usage by 90% on average, respectively. Compared with the traditional smart contract architecture, ATOM can improve EVM Memory access efficiency significantly by up to 10x and achieve improvement of execution efficiency with up to 1:6x.

The traditional covert communication channel relying on a third-party node is vulnerable to attack. The data are easily tampered with and the identity information of the communication party is fragile. Blockchain has the characteristics of decentralization and tamper resistance, which can effectively solve the above problems. In addition, some confidential information needs to be transmitted covertly in the transparent blockchain. A smart contract deployed in the blockchain to automatically realize its function can replace a centralized node to provide credible guarantee for communication. The diversity of parameters, data redundancy, and code programmability of smart contract make it an excellent carrier for covert communication under blockchain. In this article, we propose a covert communication model combined with smart contracts to covertly transfer information in the blockchain environment. To implement this model, we use the parameters in the contract to map the secret information sequence, and call the contract to transfer message. Voting contract and secret bidding contract are combined to instantiate the proposed model, and optimized versions of the two contracts are also proposed to reduce costs. Moreover, we use encryption algorithms and two-round protocols to ensure data privacy and design corresponding information embedding and transmission methods for different scenarios. To improve the concealment of communication, redundant options, effective price ranges, and invalid bids are set in two contracts, respectively. The experimental results show that the proposed model has tamper resistance and low complexity, and it is feasible to use this model for covert communication.

One of the main concerns of cybersecurity is the detection of hoaxes across social media. Hoaxers propagate such messages to mislead users and to promote violence. Several approaches exist in literature to address this issue. They are mainly limited to detect hoax activities by characterizing the message nature and detecting provenance of messages. However, unless hoaxes are detected, they continue to propagate across social media nodes. This work aims at reducing the dissemination of hoaxes across group of users. Relying on social graph structure, this research develops a mechanism based on smart contract logics to prevent a group to consume a fake post. To achieve this objective, we used a smart contract to exploit a trust index computed based on message characteristics and group features such as graph density, group status, group degree, group acceptability. Based on the value of trust index, the message is forwarded or blocked. Experiments realized on groups of different characteristics revealed that the proposed smart contract is even able to reactively block a fake post of the same nature than the group type. Results indicate that the proportion of targeted groups could be reduced even if their interests match with the message subject. This research is an important step forward to anti-promote hoaxes with the novelty of exploiting smart contract approach to contain their propagation.

Ethereum is one of the most popular blockchain systems that support more than half a million transactions every day and foster miscellaneous decentralized applications with its Turing-complete smart contract machine. Whereas it remains mysterious what the transaction pattern of Ethereum is and how it evolves over time. In this article, we study the evolutionary behavior of Ethereum transactions from a temporal graph point of view. We first develop a data analytic platform to collect external transactions associated with users as well as internal transactions initiated by smart contracts. Three types of temporal graphs, user-to-user, contract-to-contract, and user-contract graphs, are constructed according to trading relationships and are segmented with an appropriate time window. We observe a strong correlation between the size of the user-to-user transaction graph and the average Ether price in a time window, while no evidence of such linkage is shown at the average degree, average edge weights, and average triplet closure duration. The macroscopic and microscopic burstiness of Ethereum transactions are validated. We analyze the Gini indexes of the transaction graphs and the user wealth in which Ethereum is found to be very unfair since the very beginning, in a sense, ``the rich is already very rich.''

With the development of electric vehicles, the shortage of charging piles has gradually been exposed. In response to this situation, charging pile (CP) operators have taken private charging piles into the shared charging system. Due to the lack of maintenance personnel for private charging piles that join shared charging, users often face the problems of damaged CPs and poor service attitudes of CP owners. Reputation solutions based on third-party platforms face a problem of single-point failures and reputation solutions based on blockchain face problems of storage and query efficiency. To improve storage and query efficiency, this paper proposes a multi-chain charging model that stores different types of information on different blockchains. However, it faces the problem of unreliable information called across chains, when calculating reputation across chains. Therefore, this paper proposes a cross-chain trusted smart contract (C2T smart contract) to ensure the authenticity, real-time, and inter-chain write mutual exclusion of cross-chain information, making reputation calculation in the multi-chain charging model more convenient and more accurate. Specially, we propose a data mutual trust mechanism based on merkel proof to ensure the authenticity of cross-chain information and prevent forged information from participating in calculating reputation. Furthermore, we present a data structure composed of multiple counting bloom filters (MCBF) to verify the real-time of information and filter out non-real-time information, thereby ensuring the real-time of the calculated reputation. In addition, we put forward an algorithm to guarantee the inter-chain write mutual exclusion by hash mutexes, making the reputation calculation process more accurate and complete. The security analysis and experimental results demonstrate that C2T smart contract is feasible in practice.

Millions of smart contracts have been deployed onto Ethereum for providing various services, which can be invoked through their functions. For this purpose, the caller needs to know the function signature of a callee, which includes its function id and parameter types. Such signatures are critical to many applications focusing on smart contracts, e.g., reverse engineering, fuzzing, attack detection, and profiling. Unfortunately, it is challenging to recover the function signatures from contract bytecode, since neither debug information nor type information is present in the bytecode. To address this issue, prior approaches rely on source code, or a collection of known signatures from incomplete databases or incomplete heuristic rules, which, however, are far from adequate and cannot cope with the rapid growth of new contracts. In this paper, we propose a novel solution that leverages how functions are handled by Ethereum virtual machine (EVM) to automatically recover function signatures. In particular, we exploit how smart contracts determine the functions to be invoked to locate and extract function ids, and propose a new approach named type-aware symbolic execution (TASE) that utilizes the semantics of EVM operations on parameters to identify the number and the types of parameters.Moreover, we develop SigRec, a new tool for recovering function signatures from contract bytecode without the need of source code and function signature databases. The extensive experimental results show that SigRec outperforms all existing tools, achieving an unprecedented 98.9% accuracy within 0.07 seconds. We further demonstrate that the recovered function signatures are useful in attack detection, fuzzing and reverse engineering of EVM bytecode.

With the increasing number of traffic accidents and terrorist attacks by modern vehicles, vehicular digital forensics (VDF) has gained significant attention in identifying evidence from the related digital devices. Ensuring the law enforcement agency to accurately integrate various kinds of data is a crucial point to determine the facts. However, malicious attackers or semi-honest participants may undermine the digital forensic procedures. Enabling accountability and privacy-preservation while providing secure data access control in VDF is a non-trivial challenge. To mitigate this issue, in this paper, we propose a blockchain-based decentralized solution for VDF named BB-VDF, in which the accountable protocols and privacy-preserving algorithm are constructed. The desirable security properties and fine-grained data access control are achieved based on smart contract and the customized cryptographic construction. Specifically, we design a distributed key-policy attribute based encryption scheme with partially hidden access structures, named DKP-ABE-H, to realize the secure fine-grained forensics data access control. Further, a novel smart contract is designed to model the forensics procedures as a finite state machine, which guarantees accountability that each participant performs auditable cooperation under tamper-resistant and traceable transactions. Systematic security analysis and extensive experimental results show the feasibility and practicability of our proposed BB-VDF scheme.

Industrial Internet of things (IIoT) is facing significant scalability and security challenges in sharing massive device data across heterogeneous network domains. Traditional cloud-based sharing solutions for IIoT applications that are getting closer to devices are uneconomical and inefficient because of their centralized architecture. We propose a blockchain-based data-sharing framework called EdgeShare for edge data-sharing services among heterogeneous network domains by introducing a two-layer overlay network topology and an edge computing paradigm that can dramatically alleviate system pressure and reduce communication delays to improve data-sharing efficiency. Under such framework, a novel smart contract is designed to construct access control strategies with dissimilar granularities to achieve different levels of data access control in an undeniable and automated manner. All IIoT data-sharing activities are recorded in a blockchain for secured transaction logging and auditing. We implement an EdgeShare prototype and conduct extensive experiments and analyses to evaluate the feasibility of our proposals. In contrast with centralized sharing methods, results demonstrate that the proposed framework can effectively improve the reliability, efficiency, and security of massive data sharing while introducing reasonable and acceptable overheads.

Blockchain is a distributed database shared among disparate parties. It promises to enable new applications and solutions to wide-ranging domains. However, today's blockchains suffer from low throughput and high latency. This impedes widespread adoption of more complex blockchain-based applications. We propose a new direction for the future development of blockchains: pushing utmost work to client-side to make the blockchain-core as simple as possible. To show the feasibility and practicability of this idea, we construct both client-assisted consensus and client-assisted smart contracts. The client-assisted consensus only requires a single round-trip between clients and blockchain noses; it is leaderless and parallelizable. Our experimental results show that it can process thousands of transactions per second when the number of replicas is 400. The client-assisted smart contract pushes the expensive execution to client-side and ensures the correctness by verifiable computation. It avoids duplicated execution, allows parallel execution and reduces transaction/blockchain size.

While 5G can facilitate high-speed Internet access and make over-the-horizon control a reality for unmanned aerial vehicles (UAVs; also known as drones), there are also potential security and privacy considerations, for example, authentication among drones. Centralized authentication approaches not only suffer from a single point of failure, but they are also incapable of cross-domain authentication, which complicates the cooperation of drones from different domains. To address these limitations, a blockchain-based cross-domain authentication scheme for intelligent 5G-enabled Internet of drones is proposed in this paper. Our approach employs multiple signatures based on threshold sharing to build an identity federation for collaborative domains. This allows us to support domain joining and exiting. Reliable communication between cross-domain devices is achieved by utilizing smart contract for authentication. The session keys are negotiated to secure subsequent communication between two parties. Our security and performance evaluations show that the proposed scheme is resistant to common attacks targeting Internet of Things (IoT) devices (including drones), as well as demonstrating its effectiveness and efficiency.

To make up the insufficient computing power of remote medical devices and solve the transmission delay problem, edge computing has been widely studied in academic community. The application of edge computing in E-health makes it more convenient for medical providers to view the patient’electronic health records (EHRs), which not only improves the work efficiency of medical providers, but also provides patients with more personalized treatments. However,there are some security problems in the E-health system, such as EHRs leakage or tampering. Therefore, attribute-based encryption (CP-ABE) is widely used in the access control of EHRS. In order to reduce the computing cost of resource-constrained devices and achieve more flexible fine-grained access control, this paper proposes an effective ABE scheme that outsources part of the computing load to the fog nodes, which safely shares data with lower overheads. It also supports users revocation and attribute revocation. In addition, we combine smart contract and blockchain to share more fine-grained EHRs. The security proof and experimental results show that our scheme has higher efficiency to protect the patients’ EHRs privacy.

With the development of smart cities, the chimney construction method can no longer meet service needs. It is extremely urgent to build a unified urban brain, and the core issue is data sharing and fusion. Aiming at the problems of data island, data leakage, and high trust cost in the IoT of smart city, a lightweight and trusted sharing mechanism (LTSM) is proposed. Firstly, the blockchain is combined with federated learning to realize the data sharing, which not only protects the private data, but also ensures the sharing process trust. Then, a node selection algorithm based on credit value and a node evaluation algorithm based on smart contract are designed to improve the quality of federated learning. Finally, we propose an improved raft consensus to meet the delay and security requirements of the consortium blockchain in smart city scenario. In the simulation, we evaluate the federated learning algorithm, the node selection algorithm, and the improved raft consensus respectively. The experimental results show that the LTSM mechanism has good application value. The federated learning model has a better accuracy, but its training time is also longer. The node selection algorithm is helpful to improve the accuracy of federated learning model. The improved raft consensus improves the throughput.

Peer to Peer energy trading systems faces many complexities and challenges, prosumers privacy, security threats, gapped communication, payment settlement delays, performance bottlenecks among others. With the proliferation and sophis- tication of the smart grid, Blockchain-based solutions have gained a lot of attention in securing several domains, including the energy sector. However, the majority of existing solutions either employ Ethereum-based permissionless Blockchains that are highly scalable and secure, although performance inefficient; or use Hyperledger-based permissioned Blockchains that offer high security but are plagued by poor scalability and fault- tolerant capabilities in a decentralized environment. The need for upcoming decentralized energy is a hybrid framework that can provide efficient performances with node and smart contract permissioning schemes.

Diverse technologies, such as machine learning and big data, have been driving the prosperity of the Internet of Things (IoT) and the ubiquitous proliferation of IoT devices. Consequently, it is natural that IoT becomes the driving force to meet the increasing demand for frictionless transactions. To secure transactions in IoT, blockchain is widely deployed since it can remove the necessity of a trusted central authority. However, the mainstream blockchain-based IoT payment platforms, dominated by Proof-of-Work (PoW) and Proof-of-Stake (PoS) consensus algorithms, face several major security and scalability challenges that result in system failures and financial loss. Among the three leading attacks in this scenario, double-spend attacks and long-range attacks threaten the tokens of blockchain users, while eclipse attacks target denial of service. To defeat these attacks, a novel bidirectional-linked blockchain (BLB) using chameleon hash functions is proposed, where bidirectional pointers are constructed between blocks. Furthermore, a new Committee Members Auction (CMA) consensus algorithm is designed to improve the security and attack resistance of BLB while guaranteeing high scalability. In CMA, distributed blockchain nodes elect committee members through a verifiable random function. The smart contract uses Shamir’s Secret Sharing scheme to distribute the trapdoor keys to committee members. To better investigate BLB’s resistance against double-spend attacks, an improved Nakamoto’s attack analysis is presented. In addition, a modified entropy metric is devised to measure eclipse attack resistance across different consensus algorithms. Extensive evaluation results show the superior resistance against attacks and demonstrate high scalability of BLB compared with current leading paradigms based on PoS and PoW.

Smart contracts are protocols that can automatically execute a transaction including an electronic contract when a condition is satisfied without a trusted third party. In a representative use-case, a smart contract is executed when multiple parties fairly trade on a blockchain asset. On blockchain systems, a smart contract can be regarded as a system participant, responding to the information received, receiving and storing values, and sending information and values outwards. Also, a smart contract can temporarily keep assets, and always perform operations in accordance with prior rules. Many cryptocurrencies have implemented smart contracts. At POST2018, Atzei et al. give formulations of seven fair exchange protocols using smart contract on Bitcoin: oracle, escrow, intermediated payment, timed commitment, micropayment channels, fair lotteries, and contingent payment. However, they only give an informal discussion on security.

The advent of radio access network (RAN) slicing is envisioned as a new paradigm for accommodating different virtualized networks on a single infrastructure in 5G and beyond. Consequently, infrastructure providers (InPs) desire virtualized networks to share their subleased resources for effective resource management. Nonetheless, security and privacy challenges in the wireless network deter operators from collaborating with one another for resource trading. Lately, blockchain technology has received overwhelming attention for secure resource trading thanks to its security features. This paper proposes a novel hierarchical framework for blockchain-based resource trading among peer-to-peer (P2P) mobile virtual network operators (MVNOs), for autonomous resource slicing in 5G RAN. Specifically, a consortium blockchain network that supports hyperledger smart contract (SC) is deployed to set up secure resource trading among seller and buyer MVNOs. With the aim of designing a fair incentive mechanism, we model the pricing and demand problem of the seller and buyers as a two-stage Stackelberg game, where the seller MVNO is the leader and buyer MVNOs are followers. To achieve a Stackelberg equilibrium (SE) for the formulated game, a dueling deep Q-network (Dueling DQN) scheme is designed to achieve optimal pricing and demand policies for autonomous resource allocation at negotiation interval. Comprehensive simulation results analysis prove that the proposed scheme reduces double spending attacks by 12% in resource trading settings, and maximizes the utilities of players. The proposed scheme also outperforms deep Q-Network (DQN), Q-learning (QL) and greedy algorithm (GA), in terms of slice and system level satisfaction and resource utilization.

Anomaly detection for smart contracts can effectively prevent hidden security risks such as financial fraud, illegal financing, and money laundering. Ethereum is currently the largest platform for smart contracts, and anomaly detection is imminent. However, the data related to smart contracts is huge and contains complex objects and relationships. It is impossible to extract high-order attributes and low efficiency using traditional methods. The key to reduce fraud is extracting features from complex smart contracts and effectively identifying abnormal contracts. Therefore, this paper constructs a Heterogeneous Graph Transformer Networks (S_HGTNs) suitable for smart contract anomaly detection to detect financial fraud on the Ethereum platform. To perform feature representation, this paper first extracts the features to construct a Heterogeneous Information Network (HIN) for smart contract, and uses the relationship matrix obtained from the meta-path learned in the transformer network as the input of the convolution network, and finally uses the node embedding for classification tasks. The classification results show that this model performs better than the traditional model and the standard deviation is small, which proves the effectiveness and stability of the model.

Smart contracts in Ethereum blockchain network are self-executable scripts used for managing tokenized assets and access rights between different entities on the blockchain network. They are significant addition to blockchain technology for achieving data transparency and reliability in maintaining the digital relationship between two or more entities. However, the smart contract is in its nascent stage and suffers from various limitations, including immutability and code secrecy. In this paper, we address the inability of smart contracts to access the on-chain data. The smart contract currently fetches the on-chain data by taking external oracle assistance, which can be compromised to influence the customers. To address the issue, we propose a decentralized mechanism for accessing the blockchain data directly from the smart contracts by indexing single or multiple parameters of a transaction in each block using the Merkle-Patricia Trie (MPT). The idea is to increase the data transparency of blockchain applications. The paper provides a sequential search methodology that can retrieve transactions from N blocks, satisfying specific conditions, in O(N). The complexity is further reduced to O(N/k) by partitioning the blockchain data into k disjoint subsets to achieve parallelism in the search procedure. We experimentally verify the effectiveness of the proposed methodology with a case study on the Ethereum blockchain data.

This article presents LiteZKP a framework for supporting multiple anonymous payments using a smart contract-based zero-knowledge proof (ZKP) protocol on resource-limited devices. Specifically, to address challenges related to minimizing the computational overhead and offer a fully anonymous system, LiteZKP includes novel schemes such a new merkle tree mechanism to reduce the burden of ZKP operations, and integrates smart contract-based ZKP with an off-chain payment channel to reduce the amount of ZKP operations when performing continuous data exchange. We present evaluation results of LiteZKP from both PC-scale and resource-limited client devices and our results suggest that LiteZKP reduces the latency and energy consumption by more than 55% on Internet of Things (IoT)/mobile edge computing platforms, while requiring only 8% of block processing fee compared to a naive ZKP-based scheme.

Service provisioning systems assign users to service providers according to allocation criteria that strike an optimal trade-off between users Quality of Experience (QoE) and the operation cost endured by providers. These systems have been leveraging Smart Contracts (SCs) to add trust and transparency to their criteria. However, deploying fixed allocation criteria in SCs does not necessarily lead to the best performance over time since the blockchain participants join and leave flexibly, and their load varies with time, making the original allocation suboptimal. Furthermore, updating the criteria manually at every variation in the blockchain jeopardizes the autonomous and independent execution promised by SCs. Thus, we propose a set of light-weight agents for SCs that are capable of optimizing the performance. We also propose using online learning SCs, empowered by Deep Reinforcement Learning (DRL) agent, that leverage the chained data to continuously self-tune its allocation criteria. We show that the proposed learning-assisted method achieves superior performance on the combinatorial multi-stage allocation problem while still being executable in real-time. We also compare the proposed approach with standard heuristics as well as planning methods. Results show a significant performance advantage over heuristics and better adaptability to the dynamic nature of blockchain networks. IEEE Scopus

Transparency and security in an electoral process are fundamental to the legitimacy of the results and the confidence of voters. Thus, it is necessary to assess opportunities to improve traditional voting systems. Among the main problems is the lack of transparency, due to the impossibility of a voter checking their vote and the lack of access to the source from which the results are obtained. Another problem is mobility, due to the impossibility of performing remote voting, as traditional voting systems continue to require the physical presence of the voter in an electoral zone. Thus, the objective of this work is to propose a voting system that is functional, transparent, safe, and accessible to everyone. Voters can vote through a mobile application with biometric authentication using fingerprint and password access. In our proposal, votes are registered in an Ethereum Blockchain through a Smart Contract, allowing the voter to check their vote. The results are expected to collaborate with the evolution of studies necessary to improve traditional voting systems, especially in fundamental aspects such as security, transparency, and mobility.

As the most successful application of the sharing economy, ride-hailing service is popular worldwide and serves millions of users per day worldwide. Ride-hailing service providers (SPs) usually collect users' personal data to improve their services via big data technologies. However, SPs may also use the collected user data to apply personalized prices to different users, which raises price fairness concerns. In this paper, we propose a smart price auditing system named Spas. Spas allows a user to purchase Fair Price Insurance in the form of Price Auditing Contract, then the price of ride-hailing service (RHS) order will be audited automatically once completed. According to the auditing result, the contract punishes misbehaving SPs and also compensates affected users automatically. By replacing an untrustworthy centralized auditor with carefully designed smart contracts, we construct a decentralized price auditing system which is trustworthy and transparent. We demonstrate a theoretical model for practical payment flows based on real RHS user data and we implement Spas in Hyperledger Fabric to show that decentralizing and automating price auditing for RHS with financial incentives is technically feasible.