Blockchain Papers

Hajar Moudoud, Soumaya Cherkaoui

No abstract is available for this record.

Ruoting Xiong, Wei Ren, Xiaohan Hao, Jie He · 5 authors

In large-scale Internet of Things (IoT) systems, centralized authentication can be challenging, for example in terms of identity management, authentication overhead, and single point of failure. Distributed identity management has been envisioned as a promising approach for mitigating above all, but the security and performance of the overall solution have not been extensively evaluated. In this article, we proposed a decentralized identity management scheme based on blockchain for tackling large-scale IoT such as VANET. We implement smart contracts to support large-scale user access control, together with design trust management methods for reputation evaluation and credit penalty mechanisms, which can prevent various types of attacks in distributed identification contexts. The experiment results and analysis justified that our scheme is scalable with good performance. Specifically, the system response is on a millisecond scale and all the functions consume within 250 ms. Also, the time consumption of the query maintains a manageable delay within 0.5 ms no matter the remarkable growth of users. Finally, we observe that the throughput of the current blockchain platform could meet the requirement of our scheme with the increasing number of users’ upload request in real-world simulation.

Songjiang Li, Tao Zhou, Huamin Yang, Peng Wang

The reliable circulation of automotive supply chain data is crucial for automotive manufacturers and related enterprises as it promotes efficient supply chain operations and enhances their competitiveness and sustainability. However, with the increasing prominence of privacy protection and information security issues, traditional data sharing solutions are no longer able to meet the requirements for highly reliable secure storage and flexible access control. In response to this demand, we propose a secure data storage and access control scheme for the supply chain ecosystem based on the enterprise-level blockchain platform Hyperledger Fabric. The design incorporates a dual-layer attribute-based auditable access control model for access control, with four smart contracts aimed at coordinating and implementing access policies. The experimental results demonstrate that the proposed approach exhibits significant advantages under large-scale data and multi-attribute conditions. It enables fine-grained, dynamic access control under ciphertext and maintains high throughput and security in simulated real-world operational scenarios.

Peiyun Zhang, Song Ding, Qinglin Zhao

Artificial intelligence (AI) is a very powerful technology and can be a potential disrupter and essential enabler. As AI expands into almost every aspect of our lives, people raise serious concerns about AI misbehaving and misuse. To address this concern, international organizations have put forward ethics guidelines for constructing trustworthy AI (TAI), including privacy, transparency, fairness, robustness, accountability, and so on. However, because of the black-box characteristics and complex models of AI systems, it is challenging to translate these guiding principles and aspirations into AI systems. Blockchain, an important decentralized technology, can provide the capabilities of transparency, traceability, immutability, and secure sharing and hence can be used to make AI trustworthy. In this paper, we survey studies on blockchain-based TAI (BTAI) from a software development lifecycle view. We classify the lifecycle of BTAI into four stages: Planning, data collection, model development, and system deployment/use. Particularly, we investigate and summarize the trustworthy issues that blockchain can achieve in the latter three stages, including (1) data transparency, privacy, and accountability; (2) model transparency, privacy, robustness, and fairness; and (3) robustness, privacy, transparency, and fairness of system deployment/use. Finally, we present essential open research issues and future work on developing BTAI systems.

Tao Zhang

Privacy is important to financial industry, so as to blockchain based cryptocurrencies. Bitcoin can provide only weak identity privacy. To overcome privacy challenges of Bitcoin, some privacy focused cryptocurrencies are proposed, such as Dash, Monero, Zcash, Grin and Verge. Private address, confidential transaction, and network anonymization service are adopted to improve privacy in these privacy focused cryptocurrencies. We propose four privacy metrics for blockchain based cryptocurrencies as identity anonymity, transaction confidentiality, transaction unlinkability, and network anonymity. Then make a comparative analysis on privacy of Bitcoin, Dash, Monero, Verge, Zcash, and Grin from these privacy metrics. Finally, open challenges and future directions on blockchain based privacy cryptocurrencies are discussed. In the future, multi-level privacy enhancement schemes can be combined in privacy cryptocurrencies to improve privacy, performance and scalability.

Laila Junaid, Kashif Bilal, Osman Khalid, Aiman Erbad

No abstract is available for this record.

S Sultana, Ch. Rupa, R. Pavana Malleswari, Thippa Reddy Gadekallu

In the digital age, ensuring the authenticity and security of academic certificates is a critical challenge faced by educational institutions, employers, and individuals alike. Traditional methods for verifying academic credentials are often cumbersome, time-consuming, and susceptible to fraud. However, the emergence of blockchain technology offers a promising solution to address these issues. The proposed system utilizes a blockchain network, where each academic certificate is stored as a digital asset on the blockchain. These digital certificates are cryptographically secured, timestamped, and associated with unique identifiers, such as hashes or public keys, ensuring their integrity and immutability. Anyone with access to the blockchain network can verify a certificate’s authenticity, using the MetaMask extension and Ethereum network, eliminating the need for intermediaries and reducing the risk of fraudulent credentials. The main strength of the paper is that the data that are stored in the blockchain are unique identifiers of the encrypted data, which is encrypted by using an encryption technique that provides more security to the academic certificates. Furthermore, IPFS is also used to store large amounts of encrypted data.

Xiaohong Zhang, Jiaming Lai, Ata Jahangir Moshayedi

Abstract Vehicular ad hoc networks (VANETs) is the hotspot research field of wireless mobile ad hoc network, it provides a new opportunity to create a safe and efficient transportation environment. However, as an open network where information has to interact frequently, it is difficult to ensure the security of data transmitted in VANETs and protect the privacy of drivers. Many existing information-sharing schemes use complex encryption algorithms to enable secure traffic data sharing. Nevertheless, these schemes are not suitable for VANETs because of their high computational overhead and lack of corresponding tracking mechanisms for malicious vehicles. Therefore, a traffic data security sharing scheme is designed that combines blockchain technology and traceable ring signature algorithms to secure the transmitted messages. The traceable ring signature algorithm is formulated in combination with bilinear pairing, enabling conditional privacy protection instead of traditional ring signature. To improve the efficiency of VANETs, this scheme introduces edge computing technology to reduce the computational burden of Road Side Units (RSUs) by offloading most of the computational tasks to the servers via edge nodes. In addition, we use smart contract to track malicious vehicles. Security analysis and performance comparison show that our scheme is more efficient and secure for drivers than other existing related schemes.

Cheng Chi, Zihang Yin, Yang Liu, Senchun Chai

With the continuous development of Internet of Things (IoT) technology and artificial intelligence (AI) technology, the demand for Artificial Intelligence of Things (AIoT) edge applications is increasing. However, there are challenges in AIoT edge applications, such as limited resources of edge devices, data privacy leakage, inconsistent model deployment, device authentication, and data sharing difficulties, which can affect the security and intelligence level of AIoT edge applications. Therefore, we propose a trusted cloud–edge decision architecture that ensures trustworthy authentication of terminal devices. We use lightweight deep neural network training technology to run multilayer perceptron (MLP) models on resource-limited edge devices, reducing the difficulty of model design and development. We also introduce blockchain technology to enhance the security and privacy of model and data processing. We describe the four-layer architecture and corresponding workflow details, and we introduce the main data models and focus on the core technologies of the architecture. Finally, we completed the simulation verification of the model using carbon emissions data as a sample, demonstrating the feasibility and effectiveness of the model.

Xiaoxue Yan, M.L. Chen, Yangxin Zhang, F. W. Pan

With the development of the Internet and edge computing technology, many industrial Internet applications have emerged, followed by a large amount of data in the Industrial Internet. How to securely store and share this data has become a hot topic in current research. Firstly, a blockchain and IPFS based on-chain and off-chain storage architecture is proposed, which detects data through machine learning algorithms and stores data without anomalies. Then, the roles of users were classified and various smart contracts were designed for registering users, deleting users, and managing roles and their permissions. Finally, an experimental platform was built through open-source software to verify the feasibility of the proposed scheme and realize the safe storage and sharing of normal data.

Anu Raj, Shiva Prakash

No abstract is available for this record.

Alex Márk Kovács, István András Seres

Stealth addresses are a privacy-enhancing technology that provides recipient anonymity on blockchains. In this work, we investigate the recipient anonymity and unlinkability guarantees of Umbra, the most widely used implementation of the stealth address scheme on Ethereum, and its three off-chain scalability solutions, i.e., Arbitrum, Optimism, and Polygon. Specifically, we define and evaluate four heuristics to uncover the real recipients of stealth payments. We find that for the majority of Umbra payments, it is straightforward to establish the recipient, hence nullifying the benefits of using Umbra. In particular, we identify the real recipient of 48.5%, 25.8%, 65.7%, and 52.6% of all Umbra transactions on the Ethereum main net, Polygon, Arbitrum, and Optimism networks, respectively. Finally, we suggest easily implementable countermeasures to evade our deanonymization and linking attacks.

Rim Gasmi, Sarra Hammoudi, Manal Lamri, Saad Harous

No abstract is available for this record.

Daniel Ayepah-Mensah, Guolin Sun, Gordon Owusu Boateng, Stephen Anokye · 5 authors

Radio Access Network (RAN) slicing enables resource sharing among multiple tenants and is an essential feature for next-generation mobile networks. Usually, a centralized controller aggregates available resource pools from multiple tenants to increase spectrum availability. In dynamic resource allocation, a tenant could behave strategically by adjusting its preferences based on perceived conditions to maximize its utility. Slice tenants may lie about the resources needed to gain greater utility. Such behavior could lead to poor resource utilization due to excess resources acquired by lying tenants and resource shortages because slice tenants choose not to purchase high-priced resources to save costs. Furthermore, in a scenario with many slice tenants, the centralized controller can become overwhelmed by the number of requests. This, in turn, can lead to slower response times and higher latency, resulting in poor resource utilization and QoS performance of slice tenants. Therefore, this paper proposes a peer-to-peer (P2P) approach to resource trading, where slice tenants communicate directly instead of relying on a centralized orchestrator. This design is motivated by the need for slice tenants to collaborate effectively. We model the interaction between tenants in a Stackelberg multi-leader and multi-follower game and solve the game with multi-agent deep reinforcement learning with an incentive-reward model to achieve the Stackelberg equilibrium. Furthermore, we propose a decentralized resource trading framework by integrating blockchain technology and federated deep reinforcement learning, enabling network tenants to perform inter-slice resource sharing securely. The simulation results show that the proposed mechanism has significant performance improvements over existing implementations.

Chuqiao Chen, S. B. Goyal, Anand Singh Rajawat, P. Senthil

No abstract is available for this record.

Xuanming Liu, Xinpeng Yang, Wang, Yinghao, Xun Zhang · 5 authors

Crowdsourcing has emerged as a prevalent method for mitigating the risks of correctness and security in outsourced cloud computing. This process involves an aggregator distributing tasks, collecting responses, and aggregating outcomes from multiple data sources. Such an approach harnesses the wisdom of crowds to accomplish complex tasks, enhancing the accuracy of task completion while diminishing the risks associated with the malicious actions of any single entity. However, a critical question arises: How can we ensure that the aggregator performs its role honestly and each contributor's input is fairly evaluated? In response to this challenge, we introduce a novel protocol termed $\mathsf{zkTI}. This scheme guarantees both the honest execution of the aggregation process by the aggregator and the fair evaluation of each data source. It innovatively integrates a cryptographic construct known as zero-knowledge proof with a category of truth inference algorithms for the first time. Under this protocol, the aggregation operates with both correctness and verifiability, while ensuring fair assessment of data source reliability. Experimental results demonstrate the protocol's efficiency and robustness, making it a viable and effective solution in crowdsourcing and cloud computing.

Weiwei Qi, Yu Xia, Pan Zhu, Shushu Zhang · 6 authors

Blockchain has proven in sensor networks as a distributed solution for transparent and secure storage, which allows its application in mobile wireless sensor networks (MWSNs). The consensus mechanism, an essential aspect of blockchain technology, must concern the high mobility, resource-constrained nature, and weak physical defenses of sensor nodes in MWSNs. To secure MWSN data storage in clustered communication, we design a proof-of-information (PoI) variant for fair miner campaigning via the amount of valid data generated from environmental information, including a dynamic adjustment of the data volume threshold to detect malicious nodes and prevent them from misreporting information. Additionally, we introduce a filtering mechanism through the dynamic integrated trust (DIt) of nodes, which integrates the trust evaluation of peer nodes across the network combining objective performance to prevent malicious nodes from infiltrating the final consensus group. The multi-level filtering technique improves the campaign fairness while isolating malicious nodes, ensuring complexity-sensitive PBFT algorithm efficiency in large-scale networks. Simulation results show that the scheme isolates 90% of the malicious nodes and screens 20% of members to produce a smaller final consensus group. Further analysis of impacts on the performance considering network topology and mobility patterns and comparisons of the relevant solutions are presented.

Naveen Chandra Gowda, A Bharathi Malakreddy

No abstract is available for this record.

Wei Tong, Xuewen Dong, Yushu Zhang, Zongyang Zhang · 7 authors

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.

Zhuohao Wang, Weiting Zhang, Runhu Wang, Ying Liu · 6 authors

In this paper, we focus on providing data provenance auditing schemes for distributed denial of service (DDoS) defense in intelligent internet of things (IoT). To achieve effective DDoS defense, we introduce a two-layer collaborative blockchain framework to support data auditing. Specifically, using data scattered among intelligent IoT devices, switch gateways self-assemble a layer of blockchain in the local autonomous system (AS), and the main chain with controller participation can be aggregated by its associated layer of blocks once a cycle, to obtain a global security model. To optimize the processing delay of the security model, we propose a process of data pre-validation with the goal of ensuring data consistency while satisfying overhead requirements. Since the flood of identity spoofing packets, it is difficult to solve the identity consistency of data with traditional detection methods, and accountability cannot be pursued afterwards. Thus, we proposed a Packet Traceback Telemetry (PTT) scheme, based on in-band telemetry, to solve the problem. Specifically, the PTT scheme is executed on the distributed switch side, the controller to schedule and select routing policies. Moreover, a tracing probabilistic optimization is embedded into the PTT scheme to accelerate path reconstruction and save device resources. Simulation results show that the PTT scheme can reconstruct address spoofing packet forward path, reduce the resource consumption compared with existing tracing scheme. Data tracing audit method has fine-grained detection and feasible performance.

Guo‐Qiang Zhang, Yueyue Dai, Jian Wu, Xiaojie Zhu · 5 authors

No abstract is available for this record.

Shaoyong Guo, Fan Zhang, Song Guo, Siya Xu · 5 authors

Web3 has received a lot of attention since its emergence. It aims to provide users with more diverse and vivid web services as well as the complete control over their own data. To support the development of Web3, privacy-preserving decentralized data computing schemes need to be studied. In earlier works, blockchain was used for trusted data sharing. However, due to the lack of computing attribute, blockchain is not capable enough to ensure the trustworthiness of the distributed computing process. Besides, the distributed computing method requires a large amount of data transmission and the current privacy protection researches seldom consider the problem of user privacy. In this article, we design a blockchain-assisted privacy-preserving distributed data computing architecture to break-through the limitations of existing researches. The proposed architecture ensures the secure and trustworthy computing with state channel and computing sandbox. We also design a sandbox location obfuscation method based on onion routing technology, making it difficult for attackers to identify the sandbox location or infer user privacy. Our solution fully considers the characteristics of Web3 and can well support the diverse Web3 applications.

Jiawen Kang, Jinbo Wen, Dongdong Ye, Bingkun Lai · 10 authors

Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services. The healthcare metaverses allow for effective decision-making and data analytics for users. However, there still exist critical challenges in building healthcare metaverses, such as the risk of sensitive data leakage and issues with sensing data security and freshness, as well as concerns around incentivizing data sharing. In this paper, we first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. To further improve the privacy protection of healthcare metaverses, a cross-chain empowered FL framework is utilized to enhance sensing data security. This framework utilizes a hierarchical cross-chain architecture with a main chain and multiple subchains to perform decentralized, privacy-preserving, and secure data training in both virtual and physical spaces. Moreover, we utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing in a user-centric manner. This model exploits PT to better capture the subjective utility of the service provider. Finally, our numerical results demonstrate the effectiveness of the proposed schemes for healthcare metaverses.

Kaiqi Wang, Ruiqi HONG, Yunlong Mao, Sheng ZHONG

As a promising paradigm of distributed learning, federated learning has garnered considerable attention since its emergence. However, traditional federated learning solutions based on a central server are not efficient and scalable. Moreover, the centralized design relies on a trustworthy party coordinating participants. This also leads to trust and reliability issues, such as a compromised central server or a single-point failure. To address this issue, blockchain-based federated learning has been proposed as a decentralized variant. Blockchain-based decentralized federated learning seems promising. However, a new attack surface appears. Because blockchain records each transaction on a public ledger, all peers can obtain a legal copy of the local model of each participant, severely violating the privacy and interests of the participants. Challenged by this dilemma, we provide an alternative design for secure federated learning in a decentralized way, addressing data confidentiality and fairness issues simultaneously. Unlike previous studies, we construct a produce-and-consume model for parameter aggregation on a blockchain, auditing the behavior of participants in case of free-riding and false-reporting attacks. Furthermore, we design a consensus protocol called APoS, which provides an incentive and review mechanism and enforces honest training of federated learning participants.