Online ride-hailing services (ORHS) are changing the travel mode. The quality evaluation of ORHS is essential to regulate driver behavior and guide passengers in choosing good services. The existing quality evaluation methods of ORHS rely on subjective passenger feedback, while they are susceptible to malicious or paranoid feedback, resulting in untrustworthy evaluation results. This paper proposes a non-subjective trust mechanism for ORHS to supplement existing evaluation methods. Inspired by the trust machine, this mechanism defines the concept of non-subjective trust to measure the quality of ORHS. It uses trajectory data collected by infrastructure as a parameter to calculate the non-subjective trust value of ORHS, which can ensure the trustworthiness and authenticity of the calculation results. This mechanism also improves the CKKS homomorphic encryption algorithm to ensure both the privacy protection of the trajectory data and the effective calculation of non-subjective trust values. In addition, a blockchain is adopted to store trajectory data cipher text and trust values plaintext in the infrastructure. It promotes its flexible management and use and ensures the security of original data and traceability of evaluation results. Theoretical analysis and experiments show that the trust value calculated by this mechanism is trustworthy, and its time costs are feasible.

Cross-chain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration services for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) solutions for cross-chain data exchange across multiple domains are constrained by synchronous networks, hindering efficient data exchange in intermittent network environments. Furthermore, there is a lack of research on asynchronous cross-chain transaction pool mechanisms, which are crucial for optimizing system utility. In this paper, we propose AC-BaaS, anasynchronouscross-blockchainasaservice framework tailored for the multi-domain IoT. Built upon a specially designed asynchronous sidechain architecture, the system leverages a committee to provide AC-BaaS for data exchange across multiple IoT domains. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We further propose a heuristic for optimizing the utility of the buffer pool mechanism to strike a balance between performance and resource consumption. We also examine DAS delay size settings to trade-off security and efficiency. We analyze and prove the security of AC-BaaS, simulate asynchronous communication environments under various security levels, and conduct a comprehensive evaluation. The results show that AC-BaaS outperforms SOTA schemes, improving throughput by an average of 1.71 to 5.09 times, reducing transaction latency by 64.36% to 85.49%, and maintaining comparable resource overhead.

As cloud computing gains widespread adoption, cloud storage services have become the primary means of data management for users. Authenticated data structures (ADS) are a novel computational model designed to address data authentication problems in distributed environments. With the growing demand for robust data security, vulnerability detection in storage systems has become a critical area of focus to ensure resilience against potential threats. However, traditional ADS, while ensuring consistency between cloud data and source data, have limitations in handling dynamic data operations on multiple types of files, storage space expansion, and single-point failure issues. To tackle these issues, this paper proposes a blockchain-assisted classifiable data auditing scheme with dynamic operations. First, trapdoor hash functions are used to construct a binary tree. During dynamic data operations, the impact of hash updates is confined to a subset of nodes, ensuring global stability and reducing computational resource consumption. Second, innovative data structures and verification mechanisms are introduced, reducing the risk of single-point failures by decentralizing the dependency on verification paths. Finally, data types are confirmed based on data identifiers, and corresponding path information is recorded, enabling efficient and rapid dynamic operations on specific types of files within multi-source data. Both security analysis and performance assessment demonstrate that BCDAS conducts data auditing for with reliability and efficiency.

Online ride-hailing services (ORHS) are changing the travel mode. The quality evaluation of ORHS is essential to regulate driver behavior and guide passengers in choosing good services. The existing quality evaluation methods of ORHS rely on subjective passenger feedback, while they are susceptible to malicious or paranoid feedback, resulting in untrustworthy evaluation results. This paper proposes a non-subjective trust mechanism for ORHS to supplement existing evaluation methods. Inspired by the trust machine, this mechanism defines the concept of non-subjective trust to measure the quality of ORHS. It uses trajectory data collected by infrastructure as a parameter to calculate the non-subjective trust value of ORHS, which can ensure the trustworthiness and authenticity of the calculation results. In addition, a blockchain is adopted to store trajectory data and trust values in the infrastructure. It promotes its flexible management and use and ensures the security of data and traceability of evaluation results. Security analysis and extensive experiments show that the trust value calculated by this mechanism is resistant to attacks and trustworthy.

Recent Blockchain-based Internet of Vehicles (BIoV) solutions are proposed to provide the capabilities of trust management and incentive distribution for traffic information interaction in decentralized trustless Internet of Vehicles (IoV). However, existing trust management methods in BIoV are designed based on subjective user feedback, which is vulnerable to bad-mouthing and collusion attacks. Besides, these incentive strategies achieve accurate information interaction based on the game theory, yet it is challenging for the practical IoV scenario without completely explicit parameters. To address these issues, we propose TI-BIoV, a traffic information interaction system based on three blockchains for IoV with the nonsubjective trust evaluation and optimal incentive with partial inexplicit parameters. Specifically, a nonsubjective trust mechanism is designed based on the traffic information offset calculated by other related traffic information, which ensures the change of vehicle trust value without any subjective factors. On this basis, a trust-based consensus protocol, which selects entities with high trust values as participants, is given to realize the reliable public audit of transactions. According to traffic information accuracy measurements, we develop a$Q$-learning-based algorithm to encourage vehicles continuously submit accurate traffic information and optimally schedule the incentive for both platform and vehicle via training with incompletely explicit parameters of TI-BIoV. Finally, we analyze the security properties and common attacks of TI-BIoV and implement a prototype. The experimental results show that TI-BIoV achieves reliable consensus with nonsubjective trust evaluation and runs stably for a long time with two-sided incentive strategies.

As the Internet of things (IoT) is increasingly popular, the number of IoT devices such as sensors and smart equipments are growing at an astonishing rate and data generated by these devices is exploding. However, these massive IoT data, stored in the form of isolated data centers, can not be shared by others who also need it. Moreover, data exchange is now needing a secure and fair mechanism to guarantee the data provider's rights and data security. Data providers also lack the motivation to share their data, as no effective mechanism exists to reward this behavior. To solve these problems, we propose a digital asset exchange mechanism based on blockchain technology, in which we record the behavior of data publishing and exchanging into the blockchain, which can ensure the reliability and transparency of data exchange without the restriction of trusted third-party payment institutions. Especially, to inspire the data providers to share their high-quality data, we design an incentive mechanism based on QoS, which gives higher rewards to those who provide high-quality data. Experimental results of this prototype demonstrate that this mechanism is appropriate to be applied in practice.

Internet of Things (IoT), an significant support for strategic emerging industries, is re-building industrial systems, such as transportation, healthcare and energy, while a number of new features and requirements like data cross-industry sharding appear in recent years. The emerging Blockchain technology has provided a promising opportunity to break information island and single-domain management in multi-domain IoT systems by leveraging distributed storage. On the other hand, the transaction consensus throughput in the mainstream blockchain systems, such as Ethereum and Fabric, is far from meeting the demand for massive data storage in time in multi-domain IoT systems. In this paper, we design a sharding protocol called MDIoTSP. The protocol first partitions the overall blockchains into many small shards, each of which generally recognized as a micro-blockchain according to the multiple domains, and then make the final consensus by merging the hash digests of the sub-blocks which generated from the shards for the multi-domain IoT blockchains ecosystem specifically. We also develop MicrothingsChains to run MDIoTSP in multi-domain IoT blockchains to prove our assumption that MDIoTSP scales up the transaction consensus throughput near linearly with the number of shards.