Industrial Internet of Things (IIoT) facilitate private data collecting via (a broad range of) sensors, and the analysis of such data can inform decision making at different levels. Federated learning (FL) can be used to analyze the collected data, in privacy-preserving manner by transmitting model updates instead of private data in IIoT networks. The FL framework is, however, vulnerable because model updates are easily tampered with by malicious agents. Motivated by this observation, we propose a novel chameleon hash scheme with a changeable trapdoor (CHCT) for secure FL in IIoT settings. Our scheme imposes various constraints on the use of trapdoor. We give a rigorous security analysis on our CHCT scheme. We also instantiate the CHCT scheme as a redactable medical blockchain (RMB). The experimental evaluations demonstrate the practical utility of CHCT in terms of accuracy and efficiency.
With the rapid development of blockchain, big data, cloud computing, and artificial intelligence, the security of multisource data collaborative computing has become increasingly prominent. Secure multiparty computing has become the core technology of privacy collaborative computing. Millionaires’ problem is the cornerstone of secure multiparty computation. Firstly, this paper proposes a 0-1 coding rule, which is used to solve the millionaires’ problem under the semihonest model. Aiming at the possible malicious behaviors of the protocol under the semihonest model, the millionaires’ problem protocol under the malicious model based on the elliptic curve cryptography is designed by using cryptographic tools such as the zero-knowledge proof and the cut-choose method. This protocol not only can effectively solve the millionaires’ problem but also can safely and effectively prevent malicious behaviors. Meanwhile, the security ordering designed by the protocol can be effectively applied to a quality evaluation in the blockchain.
The industrial healthcaresystem has enabled the possibility of realizing advanced real-time monitoring of patients and enriched the quality of medical services through data sharing among intelligent wearable devices and sensors. However, this connectivity brings the intrinsic vulnerabilities related to security and privacy due to the need of continuous communication and monitoring over public network (insecure channel). Motivated from the aforementioned discussions, we integrate permissioned blockchain and smart contract with deep learning (DL) techniques to design a novel secure and efficient data sharing framework named PBDL. Specifically, PBDL first has a blockchain scheme to register, verify (using zero-knowledge proof), and validate the communicating entities using the smart contract-based consensus mechanism. Second, the authenticated data are used to propose a novel DL scheme that combines stacked sparse variational autoencoder (SSVAE) with self-attention-based bidirectional long short term memory (SA-BiLSTM). In this scheme, SSVAE encodes or transforms the healthcare data into new format, and SA-BiLSTM identifies and improves the attack detection process. The security analysis and experimental results using IoT-Botnet and ToN-IoT datasets confirm the superiority of the PBDL framework over existing state-of-the-art techniques.
The vehicular social network (VSN) is an emerging mobile communication system combining a vehicle ad hoc network (VANET) with a social network. It provides a new means of sharing, disseminating, and delivering data for passengers, drivers, and vehicles. However, a VSN may expose users’ private information, such as identities, location information, and trajectories, and tampering with shared data may lead to security and safety problems in vehicle systems. Considering the security and privacy preservation of shared data, we propose a lightweight decentralized multiauthority access control scheme based on ciphertext-policy attribute-based encryption (CP-ABE) and blockchain, by which a decentralized multiauthorization node supports vehicle users by performing lightweight calculations with the assistance of the vehicle cloud service provider (VCSP). We use blockchain to record storage and access transactions, achieving self-verification by users and tamper-resistance of ciphertexts. An improved smart contract reduces the workload of verification by users and achieves privacy preservation by hiding the policy. It supports user revocation and outsourced decryption, enabling more flexibility and better performance. A security and performance analysis shows that our scheme has clear advantages over existing schemes.
Internet of Vehicles (IoVs) consist of smart vehicles, Autonomous Vehicles (AVs) as well as roadside units (RSUs) that communicate wirelessly to provide enhanced transportation services such as improved traffic efficiency and reduced traffic congestion and accidents. IoVs, however, suffer from issues of security, privacy and trust. Blockchain technology has been emerged as a decentralized approach for enhanced security without depending on trusted third parties to run services. Blockchain offers the benefits of trustworthiness, immutability, and mitigates the problem of single point of failure and other attacks. In this work, we present the state-of-the-art of Blockchain-enabled IoVs (BIoV) with a particular focus on their applications such as crowdsourcing-based applications, energy trading, traffic congestion reduction, collision and accident avoidance and infotainment and content cashing. We also present in-depth applications federated learning (FL) applications for BIoVs. The key challenges resulted from the integration of Blockchain with IoV is investigated in several domains such as edge computing, ML, and FL. Lastly, a number of open issues and challenges as well as future opportunities in the area of AI-enabled BIoV, hardware-assisted security for BIoV and quantum computing attacks on BIoV.
Francesco Bruschi, Tommaso Paulon, Vincenzo Rana, Donatella Sciuto
Blockchains offer the possibility to describe and implement processes and interactions between actors through code that can be publicly inspected, and which can be executed in a transparent and guaranteed way, without resorting to the collaboration of third party guarantors.Although transparency, together with non-censorship and non-interruptibility, are characteristics that can endow unprecedented trustworthiness to the processes implemented, in many cases they risk clashing with the privacy requirements or, more specifically, with the need to control read data access.In this work we deal with a general category of processes and interactions of importance in different sectors such as procurement: auctions and tenders. If on the one hand it is important that the widest possible audience can be convinced of the correctness of a tender, on the other it is possible that some information must remain private, such as the value of the offersIn this work we present an auction / tender protocol which on the one hand publicly guarantees the fairness of the tender, and at the same time allows to keep bids private. We also present an implementation on Ethereum Virtual Machine (EVM) environments, with which we can make quantitative cost considerations.
In this study, we propose a blockchain-based privacy-preserving vaccine passport system for the global prevention and control of infectious diseases. The system operates a double-chain framework which consists of a public blockchain and a consortium blockchain. Among them, the combination of the immutability of the public blockchain and Internet of Things (IoT) technology in the supply chain ensures the openness and transparency of the cold chain logistics records of the vaccines covering the stages from auditing to the target vaccination hospitals. The system adopts the consortium blockchain to achieve the balance between the protection of users’ vaccination privacy and auditing by the government departments. Specifically, a distributed system-based threshold signature is adopted in the vaccine qualification phase to resist collusion between the vaccine manufacturing company and vaccine approval institutions. The cryptographic tools such as the anonymous credentials, zero-knowledge protocols, and range proofs ensure that users do not disclose any private information other than proving that they have a legally valid vaccine passport when users display the vaccine passports to customs. At the same time, customs can apply various vaccine prevention policies based on the conditions on the specific vaccine passports. Regarding the security properties of the system, a formal security model is given along with the corresponding security proofs.
To cope with the challenges that posed by illegal data on the immutable blockchain, and to meet the requirements of privacy regulations, the redactable blockchains are proposed. The biggest shortcoming of the state-of-the-art redactable blockchain protocols is that they fail to consider the appearance of multiple conflicting redactions at the same time. When the protocol faces multiple conflicting requests for a specific block submitted almost simultaneously, their method will lead to conflicts.We put forward a redactable blockchain protocol that packages requests into a conflict-free set for vote-based consensus to eliminate the possibility of two different edited versions of the chain and ensure the consistency of the system. Moreover, our protocol, unbinding the redaction processing from the extension of the blockchain, can be easily integrable in blockchain systems based on proof of work and proof of stake. Furthermore, our protocol, resisting against a fully adaptive adversary, confirms the edited blocks at a constant rate.
Federated learning is an emerging paradigm to conduct the machine learning collaboratively but avoid the leakage of original data. Then, how to motivate the data owners to participate federated learning and contribute high-quality data is the crucial issue. In this article, a blockchain-based federated learning (BFL) with a reputation mechanism for high-quality model aggregation is proposed. Specifically, the blockchain transforms the federated learning into a decentralized and trustworthy manner. Over the blockchain, federated learning tasks, undertaken by smart contracts, can be conducted transparently and fairly. Besides, a reputation-constrained data contribution and reward allocation mechanism is designed to encourage data owners to participate in BFL and contribute high-quality data. The noncooperative game is adopted to analyze the behavior strategies of data owners. The existence of the unique equilibrium is proved and the equilibrium point indicates that the data owners can acquire highest reward with the contribution of the highest quality data. Thus, the model quality of BFL is guaranteed. Finally, simulations on the public data sets (MNIST and CIFAR10) demonstrate that BFL with a reputation mechanism can well promote the high-quality model aggregation of federated learning as well as can prevent malicious nodes from corrupting the training task.
Vehicular Ad hoc Networks (VANETs) are one of the pillars of the Internet of Vehicles, they provide plenty of applications ranging from safety to entertainment. Safety applications largely depend on reliable and authentic traffic-related data. However, ensuring the data reliability and authenticity is facing many challenges due mainly to the scalability of VANETs such as the high speed, the long roads, and the open nature of VANETs. This paper addresses these challenges by proposing a decentralized Blockchain-based trust management framework (BC-TMF) aiming to compute trust metrics for vehicles. These trust metrics rely on the authenticity of the messages. Each vehicle assesses the authenticity of the received messages in real-time, calculates a local trust metric for the originator of such messages, then shares it with a miner. Periodically each miner aggregates the received trust metrics into global trust metrics, then packs them in a block. To investigate the efficiency and consistency of the proposed framework, extensive simulations are conducted. The obtained results show that the proposed BC-TMF has an excellent capability in computing accurate trust metrics for vehicles. Besides, it outperforms the existing ones in terms of the accuracy of computed trust metrics, particularly for malicious vehicles.
Autonomous connected vehicles are revolutionizing the automotive industry vision and impacting their business model. This trend will likely impact other related industries and their business model such as automotive insurance companies, tier-II automotive manufactures, and automotive maintenance service companies. The growing number of connected vehicles will require new policy formulation by the government about privacy and security issues in the era of massive digitalization and automation. Organizations such as 3GPP and IEEE are evolving standards that address security issues for connected vehicles. However, since Artificial Intelligence (AI) and Machine Learning (ML) will play a significant role in 5G networks and beyond, we address the privacy and security issues with a blockchain-based intelligent digital forensics system for autonomous connected vehicles (ACVs) in a connected smart world incorporating artificial intelligence. The proposed system includes a novel algorithm for the autonomous connected vehicles and the users' security and privacy. We utilize short randomizable signatures to anonymously authenticate witness's identities and to protect the witnesses' privacy. Then, we leverage fine-grained access control based on ciphertext-policy attribute-based encryption for evidence access and evaluate the feasibility of the proposed system by simulating the proposed model, its computational costs and communication overhead by implementing a prototype on a local Ethereum blockchain network platform. The simulation results show the proof-of-concept of the proposed system. We envision the application of the proposed system to ensure security and privacy of ACVs and their security applications for use with 5G-V2X and future XG-V2X networks.
While the convergence of Artificial Intelligence (AI) techniques with improved information technology systems ensured enormous benefits to the Internet of Vehicles (IoVs) systems, it also introduced an increased amount of security and privacy threats. To ensure the security of IoVs data, privacy preservation methodologies have gained significant attention in the literature. However, these strategies also need specific adjustments and modifications to cope with the advances in IoVs design. In the interim, Federated Learning (FL) has been proven as an emerging idea to protect IoVs data privacy and security. On the other hand, Blockchain technology is showing prominent possibilities with secured, dispersed, and auditable data recording and sharing schemes. In this paper, we present a comprehensive survey on the application and implementation of Blockchain-Enabled Federated Learning frameworks for IoVs. Besides, probable issues, challenges, solutions, and future research directions for BC-Enabled FL frameworks for IoVs are also presented. This survey can further be used as the basis for developing modern BC-Enabled FL solutions to resolve different data privacy issues and scenarios of IoVs.
Seunghyeon Lee, Hong‐Woo Seok, Kirim Lee, Hoh Peter In
When surveying national reference points using a global positioning system (GPS), appropriate work regulations pertaining to the surveying time must be observed. However, such data can be modified easily, so identifying non-compliance with work regulations and forgeries is challenging. If such incidents occur in cadastral surveys, it may result in financial damages to stakeholders, such as citizens and the state. Therefore, it is necessary to improve the reliability by ensuring the integrity of the GPS positioning data and allowing anyone to track them. In this study, a prototype system was developed to record GPS data and the corrections generated during survey processes using the Ethereum blockchain network. Blockchain is a distributed ledger system that prevents the manipulation of uploaded data without the need for a centralized institution by allowing anyone to check the data. Unlike in the past, the proposed system improves the data integrity and reliability for the entire survey process through blockchain, thereby ensuring transparency of the checks using smart contract addresses.
Internet-of-Things (IoT) are increasingly operating in the zero-trust environments where any devices and systems may be compromised and hence untrusted. In addition, data collected by and sent from IoT devices may be shared with and processed by edge computing systems, in order to reduce the reliance on centralized (cloud) servers, leading to further security and privacy issues. To cope with these challenges, this paper proposes an innovative blockchain-enabled information sharing solution in zero-trust context to guarantee anonymity yet entity authentication, data privacy yet data trustworthiness, and participant stimulation yet fairness. This new solution is able to support filtering of fabricated information through smart contracts, effective voting, and consensus mechanisms, which can prevent unauthenticated participants from sharing garbage information. We also prove that the proposed solution is secure in the universal composability framework, and further evaluate its performance over an Ethereum-based blockchain platform to demonstrate its utility.
The Open Vote Network is a self-tallying decentralized e-voting protocol suitable for boardroom elections. Currently, it has two Ethereum-based implementations: the first, by McCorry et al., has a scalability issue since all the computations are performed on-chain. The second implementation, by Seifelnasr et al., solves this issue partially by assigning a part of the heavy computations to an off-chain untrusted administrator in a verifiable manner. As a side effect, this second implementation became not dispute-free; there is a need for a tally dispute phase where an observer interrupts the protocol when the administrator cheats, i.e., announces a wrong tally result. In this work, we propose a new smart contract design to tackle the problems in the previous implementations by (i) preforming all the heavy computations off-chain hence achieving higher scalability, and (ii) utilizing zero-knowledge Succinct Non-interactive Argument of Knowledge (zk-SNARK) to verify the correctness of the off-chain computations, hence maintaining the dispute-free property. To demonstrate the effectiveness of our design, we develop prototype implementations on Ethereum and conduct multiple experiments for different implementation options that show a trade-off between the zk-SNARK proof generation time and the smart contract gas cost, including an implementation in which the smart contract consumes a constant amount of gas independent of the number of voters.
With the explosive growth of interconnected smart devices and sensors, the Internet has been entering the Internet of things (IoT) era and revolutionizing many aspects of our daily life. Meanwhile, crowdsourcing has been considered as a promising technology to realize collaborative intelligence. Therefore, more and more IoT-based crowdsourcing applications are emerged to take advantages of the widely distributed IoT devices to sense, collect, and analyze data with the aim to solve complex and nontrivial tasks. However, there exist many technical challenges to be addressed in the IoT-based crowdsourcing, such as security, privacy, and incentive provision. In this paper, we propose a blockchain-based architecture as an integrated solution to realize the secure and trustworthy crowdsourcing in wireless IoT. We first overview the challenges in the traditional crowdsourcing system. Then, we briefly introduce the background of the blockchain and smart contract, and propose a blockchain-based crowdsourcing architecture. In particular, we elaborate the utilization of smart contract on the specific phases of crowdsourcing. By deploying the smart contract instance, we confirm the proposed blockchain-based architecture is feasible.
ZiXiang Nie, YuanZhenTai Long, Senlin Zhang, Yueming Lu
With the in-depth integration of traditional industries and information technology in Internet of things, wireless sensor networks are used more frequently to transmit the data generated from various application scenarios. Structural health monitoring is a scene that requires recurrent data transmission in Internet of things, and the wireless sensor networks in Internet of things not only have storage and communication capabilities, but also have computing capabilities. Therefore, the demand for intelligent and decentralized data exchange between them has increased significantly which brings challenges with respect to low data reliability, chaotic data circulation, provenance tracking, and data accountability investigating, threatening the data security of structural health monitoring in Internet of things utilization. In this article, we propose a controllable data transmission mechanism based on the consortium blockchain to content the requirements of the Internet of things scenario. We identify a version-based, fine-grained, and privacy-protected data structure and propose the corresponding smart contracts for our mechanism to ensure the trusted data transmission. To prove the feasibility of our mechanism, a prototype system is implemented based on the Hyperledger Fabric, an open-source consortium blockchain framework. Our experimental results show in practice the usability and scalability of the approach in this article.
The wide use of unmanned aerial vehicles provides a promising paradigm for improving air-ground services and applications (e.g., urban sensing, disaster relief) in air-ground integrated networks (AGINs). Digital twin (DT), which is an emerging technology that utilizes data, models, and intelligent algorithms to integrate cyber physical networks and digital virtual models, provides a real-time and dynamic simulation platform for strategy optimization and decision making in AGINs. Due to the openness and massive connectivity of AGINs, the security and reliability services in this system become an important issue. In this article, we investigate the DT envisioned secure federated aerial learning for AGINs via an aerial blockchain approach. Specifically, we propose a layered framework of DT envisioned AGINs, which comprises the construction segment, communication segment, aggregation segment, analysis segment, and operation segment. Based on this framework, we offer the applications of the proposed DT envisioned AGINs. To guarantee the security of data transmission in AGINs, we investigate the aerial blockchain-based approach for ensuring data security. Furthermore, we provide a case study of DT envisioned secure federated aerial computing in AGINs to validate the effectiveness of the proposed approach through designing the aerial blockchain and training model.
The blockchain, with its key characteristics of decentralization, persistence, anonymity, and auditability, has become a solution to overcome the overdependence and lack of trust for a traditional public key infrastructure on third-party institutions. Because of these characteristics, the blockchain is suitable for solving certain open problems in the service-oriented social network, where the unreliability of submitted reviews of service vendors can cause serious security problems. To solve the unreliability problems of submitted reviews, this paper first proposes a blockchain-based identity authentication scheme and a new trusted service evaluation model by introducing the scheme into a service evaluation model. The new trusted service evaluation model consists of the blockchain-based identity authentication scheme, evaluation submission module, and evaluation publicity module. In the proposed evaluation model, only users who have successfully been authenticated can submit reviews to service vendors. The registration and authentication records of users' identity and the reviews for service vendors are all stored in the blockchain network. The security analysis shows that this model can ensure the credibility of users' reviews for service vendors, and other users can obtain credible reviews of service vendors via the review publicity module. The experimental results also show that the proposed model has a lower review submission delay than other models.