Usage instructions: here
Table of Contents
| Publish Date | Title | Authors | Code | |
|---|---|---|---|---|
| Chainlet Orbits: Topological Address Embedding for Blockchain | Knowledge Discovery and Data Mining | The rise of cryptocurrencies like Bitcoin has not only increased trade volumes but also broadened the use of graph machine learning techniques, such as address embeddings, to analyze transactions and decipher user patterns. Traditional analysis methods rely on simple heuristics and extensive data gathering, while more advanced Graph Neural Networks encounter challenges such as scalability, poor interpretability, and label scarcity in massive blockchain transaction networks. To overcome existing techniques' computational and interpretability limitations, we introduce a topological approach, Chainlet Orbits, which embeds blockchain addresses by leveraging their topological characteristics in temporal transactions. We employ our innovative address embeddings to investigate financial behavior and e-crime in the Bitcoin and Ethereum networks, focusing on distinctive substructures that arise from user behavior. Our model demonstrates exceptional performance in node classification experiments compared to GNN-based approaches. Furthermore, our approach embeds all daily nodes of the largest blockchain transaction network, Bitcoin, and creates explainable machine learning models in less than 17 minutes which takes days for GNN-based approaches. | ||
| ProSecutor: Protecting Mobile AIGC Services on Two-Layer Blockchain via Reputation and Contract Theoretic Approaches | IEEE Transactions on Mobile Computing | Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Providers (MASPs), ubiquitous and low-latency AIGC services for clients can be realized. Nonetheless, the interactions between clients and MASPs in public mobile networks, pertaining to three key mechanisms, namely MASP selection, payment scheme, and fee-ownership transfer, are unprotected. In this paper, we design the above mechanisms using a systematic approach and present the first blockchain to protect mobile AIGC, called ProSecutor. Specifically, by roll-up and layer-2 channels, ProSecutor f 8000 orms a two-layer architecture, realizing tamper-proof data recording and atomic fee-ownership transfer with high resource efficiency. Then, we present the Objective-Subjective Service Assessment (OS^{2}A) framework, which effectively evaluates the AIGC services by fusing the objective service quality with the reputation-based subjective experience of the service outcome (i.e., AIGC outputs). Deploying OS^{2}A on ProSecutor, firstly, the MASP selection can be realized by sorting the reputation. Afterward, the contract theory is adopted to optimize the payment scheme and help clients avoid moral hazards in mobile networks. We implement the prototype of ProSecutor on BlockEmulator.Extensive experiments demonstrate that ProSecutor achieves 12.5x throughput and saves 67.5% storage resources compared with BlockEmulator. Moreover, the effectiveness and efficiency of the proposed mechanisms are validated. | ||
| StarCross: Redactable blockchain-based secure and lightweight data sharing framework for satellite-based IoT | Comput. Networks | None | ||
| Blockchain-Enabled Multiple Sensitive Task-Offloading Mechanism for MEC Applications | IEEE Transactions on Mobile Computing | None | ||
| Healthcare Applications Using Blockchain With a Cloud-Assisted Decentralized Privacy-Preserving Framework | IEEE Transactions on Mobile Computing | In recent times, cloud-enabled healthcare services have gained much attention in fulfilling the analysis of privacy risks associated with effective decision management systems including trustworthiness and secure data sharing. This system has revolutionized the medical architecture to evolve unprecedented opportunities in using the technologies of next-generation networks, including services, control, and signaling, to effectively improve content delivery to individuals and organizations. However, it is a highly complex matter to distribute confidential data over a public network because data privacy and device security are at risk. As a result, a cloud-based healthcare application is introduced that integrates the computation capabilities of ubiquitous computing to provide extensive communications over dedicated Internet access in order to store health records. To manage access control mechanisms and process sensitive data without extensive computation, the existing cloud-centric systems access remote servers via dedicated networks. Based on a centralized architecture, a dedicated network uses different application domains to deliver information to the healthcare industry. Unfortunately, computation complexity greatly deteriorates the performance of peer-to-peer (P2P) communications. Thus, this paper presents a cloud-assisted decentralized privacy preserving framework (CA-DPPF) using blockchain and key agreement (KA) mechanisms to achieve secure data storage and privacy. The detailed security analysis proves that the proposed scheme fulfills the desired security properties of healthcare supply chain management (H-SCM) such as conditional traceability, data immutability, and data integrity. Overall, exploratory analysis shows that CA-DPPF guarantees better transaction efficiencies such as less latency and more throughput in order to improve the service utilization factor. | ||
| Decentralized and Privacy-Preserving Smart Parking With Secure Repetition and Full Verifiability | IEEE Transactions on Mobile Computing | Smart Parking Services (SPSs) enable cruising drivers to find the nearest parking lot with available spots, reducing the traveling time, gas, and traffic congestion. However, drivers risk the exposure of sensitive location data during parking query to an untrusted Smart Parking Service Provider (SPSP). Our motivation arises from a repetitive query to an updated database, i.e., how a driver can be repetitively paired with a previously-matched-but-forgotten lot. Meanwhile, we aim to achieve repetitive query in an oblivious and unlinkable manner. In this work, we present Mnemosyne $^{2}$mml:mathmml:msupmml:mrow/mml:mn2</mml:mn></mml:msup></mml:math>: decentralized and privacy-preserving smart parking with secure repetition and full verifiability. Specifically, we design repetitive, oblivious, and unlinkable Secure $k$mml:mathmml:mik</mml:mi></mml:math> Nearest Neighbor (S$k$mml:mathmml:mik</mml:mi></mml:math> NN) with basic verifiability (correctness and completeness) for encrypted-and-updated databases. We build a local Ethereum blockchain to perform driver-lot matching via smart contracts. To adapt to the lot count update, we resort to the immutable blockchain for advanced verifiability (truthfulness). Last, we utilize decentralized blacklistable anonymous credentials to guarantee identity privacy. Finally, we formally define and prove privacy and security. We conduct extensive experiments over a real-world dataset and compare Mnemosyne$^{2}$ mml:mathmml:msupmml:mrow/mml:mn2</mml:mn></mml:msup></mml:math> with existing work. The results show that a query only needs 8 seconds (175 ms) on average for service waiting (verification) among 500 drivers. | ||
| ParallelEVM: Operation-Level Concurrent Transaction Execution for EVM-Compatible Blockchains | European Conference on Computer Systems | None | ||
| An End-to-End Performance Comparison of Seven Permissioned Blockchain Systems | International Middleware Conference | The emergence of numerous blockchain solutions, offering innovative approaches to optimise performance, scalability, privacy, and governance, complicates performance analysis. Reasons for the difficulty of benchmarking blockchains include, for example, the high number of system parameters to configure and the effort to deploy a blockchain network. In addition, performance data, which mostly comes from system vendors, is often opaque. We provide a reproducible evaluation of the performance of seven permissioned blockchain systems across different parameter settings. We employ an end-to-end approach, where the clients sending the transactions are fully involved in the data collection approach. Our results underscore the unique characteristics and limitations of the systems we examined. Due to the insights given, our work forms the basis for continued research to optimise the performance of blockchain systems. | ||
| Optimization of Models and Strategies for Computation Offloading in the Internet of Vehicles: Efficiency and Trust | IEEE Transactions on Mobile Computing | None | ||
| Blockchain Assisted Trust Management for Data-Parallel Distributed Learning | IEEE Transactions on Mobile Computing | Machine learning models can support decision-making in mobile terminals (MTs) deployments, but their training generally requires massive datasets and abundant computation resources. This is challenging in practice due to the resource constraints of many MTs. To address this issue, data-parallel distributed learning can be conducted by offloading computation tasks from MTs to the edge-layer nodes. To facilitate the establishment of trust, one can leverage trust management, say to use trust values derived from local model quality and evaluations by other nodes as access criteria. Nonetheless, security and performance considerations remain unsolved. In this paper, we propose a blockchain-assisted dynamic trust management scheme for distributed learning, which comprises nodes attributes registration, trust calculation, information saving, and block writing. The proof of stake (PoS) consensus mechanism is leveraged to enable efficient consensus among the nodes using trust values as stakes. The incentive mechanism and corresponding dynamic optimization are then proposed to further improve system performance and security. The reinforcement-learning approach is leveraged to provide the optimal strategy for nodes’ local iterations and selection. Simulations and security analysis demonstrate that our proposed scheme can achieve an optimal trade-off between efficiency and quality of distributed learning while maintaining system security. | ||
| On State Transition Probability and Performance of Direct Acyclic Graph Based Ledgers | IEEE Transactions on Mobile Computing | Direct acyclic graph (DAG) based ledgers with multi-chain structures aim to solve the technical bottlenecks associated with classical blockchain technologies in the Internet of Things (IoT). The basic working principle of DAG-based ledgers is to validate new transactions by previous transactions in order to be added to the system. During the tip selection process in the unsteady regime, the state transition probability refers to the probability of a transaction changing from the initial state to an arbitrary state. The state transition probability plays an indispensable role in the performance and security analysis of the IoT relying on DAG-based ledgers. In this paper, we derive the exact expression and an approximate expression of the state transition probability, which both are in closed form. In addition, we propose and analyze three performance metrics, i.e., the expected cumulative weight, the expected number of steps, and the confirmation failure probability, which are derived from the state transition probability and greatly enrich the performance analysis and evaluation of the IoT. Markov chain Monte Carlo (MCMC) simulations are carried out to verify the derived analytical results and provide insight into the IoT using DAG-based ledgers. | ||
| LBDT: A Lightweight Blockchain-Based Data Trading Scheme in Internet of Vehicles Using Proof-of-Reputation | IEEE Transactions on Mobile Computing | None | ||
| TRIMP: Three-Sided Stable Matching for Distributed Vehicle Sharing System Using Stackelberg Game | IEEE Transactions on Mobile Computing | Distributed Vehicle Sharing System (DVSS) leverages emerging technologies such as blockchain to create a secure, transparent, and efficient platform for sharing vehicles. In such a system, both efficient matching of users with available vehicles and optimal pricing mechanisms play crucial roles in maximizing system revenue. However, most existing schemes utilize user-to-vehicle (two-sided) matching and pricing, which are unrealistic for DVSS due to the lack of participation of service providers. To address this issue, we propose in this paper a novel Three-sided stable Matching with an optimal Pricing (TRIMP) scheme. First, to achieve maximum utilities for all three parties simultaneously, we formulate the optimal policy and pricing problem as a three-stage Stackelberg game and derive its equilibrium points accordingly. Second, relying on these solutions from the Stackelberg game, we construct a three-sided cyclic matching for DVSS. Third, as the existence of such a matching is NP-complete, we design a specific vehicle sharing algorithm to realize stable matching. Extensive experiments demonstrate the effectiveness of our TRIMP scheme, which optimizes the matching process and ensures efficient resource allocation, leading to a more stable and well-functioning decentralized vehicle sharing ecosystem. | ||
| Integrated deep learning and blockchain-based framework for cloud manufacturing with improved customer satisfaction | Knowledge and Information Systems | None | ||
| A priority-based blockchain transaction packaging algorithm in a cloud-edge-end collaboration computing environment | Knowledge and Information Systems | None | ||
| DBUP: Dynamic blockchain UTXO processing for storage efficiency optimization | Comput. Networks | None | ||
| Trustworthy confidential virtual machines for the masses | International Middleware Conference | Confidential computing alleviates the concerns of distrustful customers by removing the cloud provider from their trusted computing base and resolves their disincentive to migrate their workloads to the cloud. This is facilitated by new hardware extensions, like AMD's SEV Secure Nested Paging (SEV-SNP), which can run a whole virtual machine with confidentiality and integrity protection against a potentially malicious hypervisor owned by an untrusted cloud provider. However, the assurance of such protection to either the service providers deploying sensitive workloads or the end-users passing sensitive data to services requires sending proof to the interested parties. Service providers can retrieve such proof by performing remote attestation while end-users have typically no means to acquire this proof or validate its correctness and therefore have to rely on the trustworthiness of the service providers. In this paper, we present Revelio, an approach that features two main contributions: i) it allows confidential virtual machine (VM)-based workloads to be designed and deployed in a way that disallows any tampering even by the service providers and ii) it empowers users to easily validate their integrity. In particular, we focus on web-facing workloads, protect them leveraging SEV-SNP, and enable end-users to remotely attest them seamlessly each time a new web session is established. To highlight the benefits of Revelio, we discuss how a standalone stateful VM that hosts an open-source collaboration office suite can be secured and present a replicated protocol proxy that enables commodity users to securely access the Internet Computer, a decentralized blockchain infrastructure. | ||
| The Fourth International Workshop on Smart Data for Blockchain and Distributed Ledger (SDBD'24) | Knowledge Discovery and Data Mining | None | ||
| Graph Neural Network-Enhanced Reinforcement Learning for Payment Channel Rebalancing | IEEE Transactions on Mobile Computing | Building on top of blockchain, payment channel networks-backed (PCNs) cryptocurrencies emerge as a promising solution for a mobile payment system with fewer intermediaries, more security, higher speed, and lower cost. A key problem for PCN is payment channel rebalancing, that is, finding a set of circular transactions that restore a PCN with skewed channel balances back into an equilibrium state. However, existing practice on payment channel rebalancing either has a hard limit on the problem size or tends to fall into local optimum. To address these challenges, we propose DRL-PCR, a Deep Reinforcement Learning-based Payment Channel Rebalancing algorithm. On one hand, DRL-PCR leverages the strong approximation ability of deep neural networks to handle large problem spaces. On the other hand, DRL-PCR decomposes the rebalancing problem into a sequence of decision-making problems and progressively builds the final solution. By aiming to find a globally optimized solution and solving the long-term optimization model of DRL, DRL-PCR is superior to greedy-based algorithms and can mitigate the risk of getting trapped in a local optimum. In particular, payment channel rebalancing typically involves dealing with graph-structured data, where the major obstacle lies in understanding the sophisticated circular dependencies between payment channels and routing paths. DRL-PCR achieves this by encoding the input data with a novel graph neural network-based model and capturing the circular dependencies through a customized message passing process. In addition, considering the distributed nature of PCN, DRL-PCR uses a leadership election protocol to elect leaders for decision-making. Evaluations on the historical data of two real-world PCNs demonstrate that DRL-PCR can restore the PCN to a more balanced state and improve the transaction throughput and success ratios by up to 2.1x and 1.6x, respectively. | ||
| Attribute-Based Data Sharing Scheme Using Blockchain for 6G-Enabled VANETs | IEEE Transactions on Mobile Computing | The advent of 6 G communications technology will bring about a transition from the “Internet of Everything” to the “Intelligent Connection of Everything”. 6G-enabled vehicular ad hoc networks (VANETs) will enjoy lower latency, higher speed, and greater capacity network services. Nevertheless, achieving secure data sharing will be an even tougher challenge. Given this, we propose an attribute-based data sharing scheme with blockchain for 6G-enabled VANETs. First, we propose an efficient multi-tree-based user revocation mechanism. With the Chinese remainder theorem, our mechanism supports user batch revocation and batch joining. Second, we achieve distributed data storage by utilizing the blockchain and smart contracts. To solve the problem of insufficient storage capacity on the blockchain, we adopt a combination of on-chain and off-chain storage. Third, to reduce the computation burden on users, our proposal supports online/offline encryption and verifiable outsourced decryption. Meanwhile, our mechanism supports policy hiding, data revocation, and cross-domain data sharing. The proposed scheme is proven to satisfy the indistinguishability under chosen plaintext attack (IND-CPA) in the standard model. Theoretical analysis shows that our mechanism outperforms existing schemes in functionality and security. Simulation experiments show that our proposal is efficient and suitable for 6G-enabled VANETs. | ||
| Blockchain-Secured Task Offloading and Resource Allocation for Cloud-Edge-End Cooperative Networks | IEEE Transactions on Mobile Computing | Enhanced by blockchain and cloud-edge-end cooperation, an edge computing network is capable to provide IoT (Internet of Things) devices higher task processing performance and better security and privacy guarantee. However, the joint resource management for both the task offloading and the blockchain services was less fully studied by existing works. To this end, in this paper, we focus on the task processing delay and energy consumption optimization problem in a multi-device and multi-base-station cloud-edge-end cooperative network. The task offloading, transmit power allocation, transmission rate allocation, and computing resource allocation are jointly optimized to minimize the long-term average total task processing delay of the tasks of all the devices while keeping the stability of the energy consumption of the devices and guaranteeing that the block mining speed matches the task offloading processes. We transform the optimization problem based on the Lyapunov optimization theory, and then design a hybrid deep reinforcement learning (DRL)-based algorithm. We decompose the problem into multiple sub-problems, and then embed multiple fast numerical methods into the twin delayed deep deterministic policy gradient (TD3) architecture as optimization subroutines to improve the learning performance of the DRL model. We also design a distributed deployment scheme for the algorithm and analyze the algorithm complexity. We demonstrate the superior performance of our algorithm in comparison with 5 reference schemes via extensive experiments in 7 scenarios. | ||
| Coral: A blockchain protocol for handling transactions with deadline constraints | Comput. Networks | None | ||
| DBCPA: Dual Blockchain-Assisted Conditional Privacy-Preserving Authentication Framework and Protocol for Vehicular Ad Hoc Networks | IEEE Transactions on Mobile Computing | Vehicular ad hoc networks (VANETs) connect all vehicles through wireless channels. They provide extensive real-time traffic information services that improve driving safety and traffic management efficiency. However, VANETs are vulnerable to security attacks because of the open wireless nature of their communication channels. Most security mechanisms for traditional VANETs are centralized and have certain limitations in satisfying security requirements, such as anti-single-point failure, distributed security authentication of messages, and privacy preservation in VANETs. To address these issues, herein, we propose a dual blockchain-assisted conditional privacy-preserving authentication framework and protocol for VANETs. The identity authentication and privacy preservation of vehicles in VANETs can be realized without relying on a centralized trusted third party. The proposed scheme also allows for the conditional tracking of illegal vehicles. The decentralized dynamic revocation of illegal vehicles can be realized through smart contracts, rendering the scheme efficient and scalable. We implement this scheme in an Ethereum test network to demonstrate its feasibility and conduct an in-depth security analysis and comprehensive performance evaluation of the proposed scheme. The results demonstrate that the proposed scheme is an effective solution for the development of a decentralized authentication system for VANETs. | ||
| AS-T3BP: An efficient assignment scheme for space TT&C tasks with bidirectional privacy-preserving under blockchain architecture | Comput. Networks | None | ||
| A Chebyshev Polynomial-Based Authentication Scheme Using Blockchain Technology for Fog-Based Vehicular Network | IEEE Transactions on Mobile Computing | The increasing number of vehicles has resulted in a tremendous growth of data in vehicular communications. Cloud-based models are inefficient for handling this large data due to high latency and bandwidth requirements. To address this, fog computing-based vehicular network models have been proposed for low latency and immediate responses. However, securing data transfer between fog servers and vehicles on public channels is essential to prevent malicious attacks. Existing authenticated key agreement schemes provide security but often incur high computational costs and vulnerability to attacks. Thus, this paper proposes a blockchain-based authenticated key agreement scheme for the fog computing-enabled vehicular network. The proposed scheme integrates blockchain into fog-based Vehicular Ad-hoc Network (VANET), where fog servers and the cloud servers serve as blockchain nodes for seamless re-authentication of a moving vehicle. For secure communication, the proposed scheme uses the Chebyshev polynomial to achieve low computational cost and establishes a common session key between cloud server, fog server, and vehicle. The formal security proof of the proposed scheme is carried out using Real-Or-Random (ROR) model. Finally, the Hyperledger Fabric and cryptographic libraries have been used for the experimental analysis to demonstrate the proposed scheme's communication and computational efficiency. | ||
| Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning | Knowledge and Information Systems | While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with cryptographic techniques, decentralized technologies introduce a novel computing paradigm. Blockchain ensures secure, transparent, and tamper-proof data management by validating and recording transactions via consensus across network nodes. Federated Learning (FL), as a distributed machine learning framework, enables participants to collaboratively train models while safeguarding data privacy by avoiding direct raw data exchange. Despite the growing interest in decentralized methods, their application in FL remains underexplored. This paper presents a thorough investigation into Blockchain-based FL (BCFL), spotlighting the synergy between blockchain's security features and FL's privacy-preserving model training capabilities. First, we present the taxonomy of BCFL from three aspects, including decentralized, separate networks, and reputation-based architectures. Then, we summarize the general architecture of BCFL systems, providing a comprehensive perspective on FL architectures informed by blockchain. Afterward, we analyze the application of BCFL in healthcare, IoT, and other privacy-sensitive areas. Finally, we identify future research directions of BCFL. | ||
| Less payment and higher efficiency: A verifiable, fair and forward-secure range query scheme using blockchain | Comput. Networks | None | ||
| EVFL-DCs: Enhancing verifiability of federated learning by double commitments based on blockchain | Comput. Networks | None | ||
| Improving Smart Contract Security with Contrastive Learning-based Vulnerability Detection | International Conference on Software Engineering | Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep learning to mitigate this threat. They treat each input contract as an independent entity and feed it into a deep learning model to learn vulnerability patterns by fitting vulnerability labels. It is a pity that they disregard the correlation between contracts, failing to consider the commonalities between contracts of the same type and the differences among contracts of different types. As a result, the performance of these methods falls short of the desired level. To tackle this problem, we propose a novel Contrastive Learning Enhanced Automated Recognition Approach for Smart Contract Vulnerabilities, named Clear. In particular, Clear employs a contrastive learning (CL) model to capture the fine-grained correlation information among contracts and generates correlation labels based on the relationships between contracts to guide the training process of the CL model. Finally, it combines the correlation and the semantic information of the contract to detect SCVs. Through an empirical evaluation of a large-scale real-world dataset of over 40K smart contracts and compare 13 state-of-the-art baseline methods. We show that Clear achieves (1) optimal performance over all baseline methods; (2) 9.73%-39.99% higher F1-score than existing deep learning methods. | ||
| Vehicloak: A Blockchain-Enabled Privacy-Preserving Payment Scheme for Location-Based Vehicular Services | IEEE Transactions on Mobile Computing | The Internet of Vehicles (IoV) technology enables vehicles to communicate with each other, with pedestrians and with roadside infrastructures, to realize more efficient, safer and more environmentally friendly transportation. IoV also promises rich location-based services for vehicles, such as parking and toll highway. However, preserving privacy for location-based service payments emerges as a critical and challenging problem in IoV. Existing schemes rely on centralized banks for payment processing, resulting in location privacy leakage to centralized entities. In this article, we propose a decentralized privacy-preserving payment scheme named Vehicloak for IoV based on the blockchain technology. The biggest challenge is to provide location privacy for vehicles while guaranteeing correct service payments using the transparent blockchain. To tackle this challenge, we introduce a new cryptographic technique called zk-GSigproof that integrates zero-knowledge proof with group signature. Vehicloak implements this technique in a smart contract to process payment, which verifies zero-knowledge proof and group signature without leaking location information. It is not limited to IoV and can be applied in many payment scenarios. To evaluate the performance of our scheme, we implement Vehicloak on a private blockchain of 100 nodes on Aliyun, and conduct a test with up to 4,000 transactions. The experimental results prove the feasibility of Vehicloak. | ||
| A Threshold-Based Full-Decentralized Authentication and Key Agreement Scheme for VANETs Powered by Consortium Blockchain | IEEE Transactions on Mobile Computing | The authentication and key agreement (AKA) scheme for VANETs can produce a series of short-term session keys, which can be used to secure the vehicular communications across open and insecure wireless channels. Traditional VANETs AKA schemes tend to employ the centralized trust architecture as the core authentication backend, which raises concerns about system security and reliability. Recently, several VANETs AKA schemes that are constructed on decentralized trust architecture have been proposed. However, these schemes do not achieve full decentralization and tend to suffer from key exposure issues, insufficient performance, and lack of optimization for on-chain storage costs. To address these shortcomings, we propose a threshold-based full-decentralized VANETs AKA scheme that is powered by consortium blockchain. In our proposed scheme, the threshold-based voting concept is employed to mitigate the key exposure issue inherent to the network infrastructure. Furthermore, we leverage lightweight cryptography in conjunction with the Cuckoo filter to reduce computational, communication, and on-chain operation costs brought by cryptographic operations and smart contracts. The security proof together with the cryptographic protocol validation tool prove the security of our proposed scheme, whereas the simulation experiment demonstrates the efficiency of our proposed scheme. | ||
| Securing the internet's backbone: A blockchain-based and incentive-driven architecture for DNS cache poisoning defense | Comput. Networks | None | ||
| TBAC: A Tokoin-Based Accountable Access Control Scheme for the Internet of Things | IEEE Transactions on Mobile Computing | Overprivilege Attack, a widely reported phenomenon in IoT that accesses unauthorized or excessive resources, is notoriously hard to prevent, trace and mitigate. In this paper, we propose TBAC, a Tokoin-Based Access Control model enabled by blockchain and Trusted Execution Environment (TEE) technologies, to offer fine-grained access control and strong auditability for IoT. TBAC materializes the virtual access power into a definite-amount, secure and accountable cryptographic coin, termed “tokoin” (token+coin), and manages it using atomic and accountable state-transition functions in a blockchain. A tokoin carries a fine-grained policy defined by the resource owner to specify the requirements to be satisfied before an access is granted, and the behavioral constraints that describe the correct procedure to follow during access. The strong-auditability is achieved with blockchain and a TEE-enabled trusted access control object (TACO) to ensure that all access activities are securely monitored and auditable. We prototy 8000 pe TBAC by implementing all its functions with well-studied cryptographic primitives over different blockchain platforms, building a TACO on top of the ARM Cortex-M33 TEE microcontroller, and constructing a user-friendly APP for regular users. A case study is finally presented to demonstrate how TBAC is employed to enable autonomous and secure in-home cargo delivery. | ||
| A seamless authentication for intra and inter metaverse platforms using blockchain | Comput. Networks | None | ||
| Dual-Level Resource Provisioning and Heterogeneous Auction for Mobile Metaverse | IEEE Transactions on Mobile Computing | The development of the mobile Metaverse has garnered increasing attention in the next-generation Internet, fueled by the rapid advancements of mobile Internet, communication, and computing technologies. With the resource limitations faced by mobile Metaverse users (MUs), the mobile Metaverse market is flourishing. This market enables MUs to access high-quality immersive experiences by trading resources with Metaverse service providers (MSPs) across geographically distributed resource pools. However, the mobile Metaverse market still faces several challenges, including the hierarchical mobile Metaverse service structure, temporal dependencies, and heterogeneous incentive mechanisms. To address these problems, this paper proposes a dual-level resources trading approach for mobile Metaverse based on blockchain. This approach employs a dual-level structure consisting of resource provisioning and heterogeneous auction mechanisms. Specifically, we formulate the resource provisioning as a temporal-dependent average delay minimization problem at the low level. To solve this low-level problem, we introduce a novel algorithm called LyDif, which leverages Lyapunov optimization techniques and diffusion models. At the high level, we propose a price-guided double dutch auction (PG-DDA) mechanism to match heterogeneous resources and determine pricing strategies. The PG-DDA smart contract is deployed on a consortium blockchain platform, facilitating resource trading management and transaction monitoring. Based on a real trace of edge-cloud service requests, our experimental results demonstrate the effectiveness of our proposed scheme in achieving optimal latency and social welfare. | ||
| BitLINK: Temporal Linkage of Address Clusters in Bitcoin Blockchain | Knowledge Discovery and Data Mining | None | ||
| A Decentralized Authenticated Key Agreement Scheme Based on Smart Contract for Securing Vehicular Ad-Hoc Networks | IEEE Transactions on Mobile Computing | Since the communication channels in vehicular ad-hoc networks (VANETs) are wireless and open, malicious adversaries can monitor or fabricate messages transmitted across them. To secure vehicular communications, an authenticated key agreement (AKA) scheme needs to be designed for VNAETs. Traditional VANETs AKA schemes require the trusted authority (TA) to authenticate the legality of message and corresponding sender. However, the TA in these schemes is vulnerable to suffer from single-point-of-failure issues. Some blockchain-based VANETs AKA schemes have been proposed recently to address the deficiency. However, these schemes rely on the consortium or private blockchain in which TAs are still required for key generation, resulting that the practicality is limited. To solve the issue, we design a smart contract-based VANETs AKA scheme, where the AKA algorithm of our proposed scheme is implemented on smart contract deployed on a public blockchain system and the TA that is responsible for key generation will not be required. The security proof and analysis show that our proposed scheme satisfies the session-key semantic security and essential security and privacy requirements, respectively. The performance analysis demonstrates that our proposed scheme outperforms existing blockchain-based VANETs AKA schemes. | ||
| Blockchain-Aided Digital Twin Offloading Mechanism in Space-Air-Ground Networks | IEEE Transactions on Mobile Computing | Space-air-ground (SAG) integrated heterogenous networks can provide pervasive intelligence services for various ground users (GUs). The network can help cellular networks release network resources and alleviate congestion pressure. Moreover, one important application of the network is that digital twin (DT) can enable nearly-instant wireless connectivity and highly-reliable data mapping from physical systems to digital world in a real-time fashion. The integration of SAG and DT (SAG-DT) reduces the gap between data analysis and physical status, which can further realize robust edge intelligence services. However, the random computation task arrival, time-varying channel gains, and the lack of mutual trust among ground GUs hinder better quality of service in the promising SAG-DT network. In this paper, we envision a SAG-DT integrated blockchain model to transfer the task data to the aerial network, and then perform the computation offloading, energy harvesting and privacy protection. Moreover, we propose a Lyapunov-aided multi-agent deep federated reinforcement learning (MADFRL) algorithm framework to optimize the CPU cycle frequency, the size of block, the number of DTs, and harvested energy to minimize the execution costs and privacy overhead. Extensive performance analyses indicate that the MADFRL algorithm framework can strengthen the data privacy via blockchain verification mechanism and approaches the optimal performance on the basis of lower computation complexity. Finally, simulation results corroborate that the proposed Lyapunov-aided MADFRL algorithm is superior to advanced benchmarks in terms of execution costs, task processing quantities and privacy overhead. | ||
| A hybrid storage blockchain-based query efficiency enhancement method for business environment evaluation | Knowledge and Information Systems | None | ||
| Selfied: Sybil defense in permissionless blockchains via in-protocol bandwidth consumption | Comput. Networks | None | ||
| Leveraging lightweight blockchain for secure collaborative computing in UAV Ad-Hoc Networks | Comput. Networks | None | ||
| Priv-Share: A privacy-preserving framework for differential and trustless delegation of cyber threat intelligence using blockchain | Comput. Networks | None | ||
| VP $^{2}$mml:mathmml:msupmml:mrow/mml:mn2</mml:mn></mml:msup></mml:math>-Match: Verifiable Privacy-Aware and Personalized Crowdsourcing Task Ma | IEEE Transactions on Mobile Computing | Privacy-aware task allocation/matching has been an active research focus in crowdsourcing. However, existing studies focus on an honest-but-curious assumption and a single-attribute matching model. There is a lack of adequate attention paid to scheme designs against malicious behaviors and supporting user-side personalized task matching over multiple attributes. A few recent works employ blockchain and cryptographic techniques to decentralize the matching procedure with verifiable and privacy-preserving on-chain executions. However, they still bear expensive on-chain overhead. In this paper, we propose VP$^{2}$mml:mathmml:msupmml:mrow/mml:mn2</mml:mn></mml:msup></mml:math>-Match, a blockchain-assisted (publicly) verifiable privacy-aware crowdsourcing task matching scheme with personalization. VP$^{2}$mml:mathmml:msupmml:mrow/mml:mn2</mml:mn></mml:msup></mml:math>-Match extends symmetric hidden vector encryption for user-side expressive matching without compromising their privacy. It avoids costly on-chain matching by letting the blockchain only store evidence/proofs for public verifiability of the matching correctness and for enforcing fair interactions against misbehaviors. Specifically, we construct extended attribute sets and solve matching verification by an algorithmic reduction into subset verification with an accumulator for proof generation. Formal security proof and extensive comparison experiments on Ethereum demonstrate the provable security and better performance of VP$^{2}$ mml:mathmml:msupmml:mrow/mml:mn2</mml:mn></mml:msup></mml:math>-Match, respectively. | ||
| Blockchain-enabled authentication framework for Maritime Transportation System empowered by 6G-IoT | Comput. Networks | None | ||
| Dolphin: Efficient Non-Blocking Consensus via Concurrent Block Generation | IEEE Transactions on Mobile Computing | Blockchain technology has become a research hotspot in distributed systems, aiming to sustain a decentralized ledger via consensus. Traditional consensus solutions exhibit slow processing speed and response time, resulting in poor performance. To address this issue, several consensus protocols have been proposed. One such popular protocol is HotStuff, a Byzantine fault-tolerant consensus (BFT) that achieves high throughput at the cost of latency. However, its throughput suffers from a proportional decrease with the increase in latency, posing a significant challenge. In this paper, we propose a new protocol called Dolphin that builds upon HotStuff. It operates in a partially synchronous network with $n$mml:mathmml:min</mml:mi></mml:math> replicas, up to $f$mml:mathmml:mif</mml:mi></mml:math> byzantine faults, where |
||
| DTPBFT:A dynamic and highly trusted blockchain consensus algorithm for UAV swarm | Comput. Networks | None | ||
| LVMT: An Efficient Authenticated Storage for Blockchain | USENIX Symposium on Operating Systems Design and Implementation | Authenticated storage access is the performance bottleneck of a blockchain, because each access can be amplified to potentially O(log n) disk I/O operations in the standard Merkle Patricia Trie (MPT) storage structure. In this article, we propose a multi-Layer Versioned Multipoint Trie (LVMT), a novel high-performance blockchain storage with significantly reduced I/O amplifications. LVMT uses the authenticated multipoint evaluation tree vector commitment protocol to update commitment proofs in constant time. LVMT adopts a multi-layer design to support unlimited key–value pairs and stores version numbers instead of value hashes to avoid costly elliptic curve multiplication operations. In our experiment, LVMT outperforms the MPT in real Ethereum traces, delivering read and write operations 6× faster. It also boosts blockchain system execution throughput by up to 2.7×. | ||
| TidyBlock: A Novel Consensus Mechanism for DAG-based Blockchain in IoT | IEEE Transactions on Mobile Computing | The integration of directed acyclic graph (DAG)-based blockchain and Internet of Things (IoT) aims at improving the efficiency of data storage. However, if massive IoT data are not placed in an organized way, the search and usage of the data for upper-level applications can be burdensome, since they have to examine the data block by block, which also increases the difficulty of data verification, affecting consensus efficiency. To maintain the high throughput advantage of DAG-based blockchain applied in IoT and improve the data analysis efficiency, we propose a novel consensus mechanism named TidyBlock, including the transaction collation mechanism for block generation and the block selection mechanism for verification. The first mechanism can tidy up scattered transactions before they are packaged into blocks, while the second one can collate blocks to facilitate verification, realizing a two-layer collation of IoT data so as to increase analysis efficiency of upper-level IoT applications. Additionally, the second mechanism can provide a self-driven incentive for rational participants to follow the first one in case they are reluctant to do extra collation work. Theoretical analysis is provided to demonstrate the validity of our proposed algorithms by formal methods. Extensive simulations based on synthetic data verify the rationality and effectiveness of the proposed mechanisms. | ||
| Auditable and Verifiable Federated Learning Based on Blockchain-Enabled Decentralization. | IEEE Transactions on Neural Networks and Learning Systems | Auditability and verifiability are critical elements in establishing trustworthiness in federated learning (FL). These principles promote transparency, accountability, and independent validation of FL processes. Incorporating auditability and verifiability is imperative for building trust and ensuring the robustness of FL methodologies. Typical FL architectures rely on a trustworthy central authority to manage the FL process. However, reliance on a central authority could become a single point of failure, making it an attractive target for cyber-attacks and insider frauds. Moreover, the central entity lacks auditability and verifiability, which undermines the privacy and security that FL aims to ensure. This article proposes an auditable and verifiable decentralized FL (DFL) framework. We first develop a smart-contract-based monitoring system for DFL participants. This monitoring system is then deployed to each DFL participant and executed when the local model training is initiated. The monitoring system records necessary information during the local training process for auditing purposes. Afterward, each DFL participant sends the local model and monitoring system to the respective blockchain node. The blockchain nodes representing each DFL participant exchange the local models and use the monitoring system to validate each local model. To ensure an auditable and verifiable decentralized aggregation procedure, we record the aggregation steps taken by each blockchain node in the aggregation contract. Following the aggregation phase, each blockchain node applies a multisignature scheme to the aggregated model, producing a globally verifiable model. Based on the signed global model and the aggregation contract, each blockchain node implements a consensus protocol to store the validated global model in tamper-proof storage. To evaluate the performance of our proposed model, we conducted a series of experiments with different machine learning architectures and datasets, including CIFAR-10, F-MNIST, and MedMNIST. The experimental results indicate a slight increase in time consumption compared with the state-of-the-art, serving as a tradeoff to ensure auditability and verifiability. The proposed blockchain-enabled DFL also saves up to 95% communication costs for the participant side. | ||
| LiteChain: A Lightweight Blockchain for Verifiable and Scalable Federated Learning in Massive Edge Networks | IEEE Transactions on Mobile Computing | None | ||
| Accelerating and Securing Blockchain-Enabled Distributed Machine Learning | IEEE Transactions on Mobile Computing | In the Internet of Things (IoT) employing centralized machine learning, security is a major concern due to the heterogeneity of end devices. Malicious devices could launch poisoning attacks to degrade machine learning models. Distributed machine learning (DML) with blockchain provides a potential solution. Once local weights are recorded on the blockchain, model aggregation with defensive schemes can be executed on smartphones to prevent attacks. However, blockchain with the proof-of-work (PoW) consensus mechanism wastes computing resources and adds latency to DML. Computing resources can be utilized more efficiently with proof-of-useful-work (uPoW), which secures transactions by solving relevant real-world problems. We propose a novel uPoW method to minimize per-round latency of DML. The uPoW mining process schedules DML instances among multi-access edge computing (MEC) servers by solving a multi-way number partitioning problem. Moreover, poisoning attacks on heterogeneous training data pose significant challenges to blockchain-based DML. To address this problem, we propose a novel aggregation protocol, named |
||
| PartChain: Scaling blockchain through account-based partitioned sharding | Comput. Networks | None | ||
| BlockSense: Towards Trustworthy Mobile Crowdsensing via Proof-of-Data Blockchain | IEEE Transactions on Mobile Computing | Mobile crowdsensing (MCS) can promote data acquisition and sharing among mobile devices. Traditional MCS platforms are based on a triangular structure consisting of three roles: data requester, worker (i.e., sensory data provider) and MCS platform. However, this centralized architecture suffers from poor reliability and difficulties in guaranteeing data quality and privacy, even provides unfair incentives for users. In this article, we propose a blockchain-based MCS platform, namely BlockSense, to replace the traditional triangular architecture of MCS models by a decentralized paradigm. To achieve the goal of trustworthiness of BlockSense, we present a novel consensus protocol, namely Proof-of-Data (PoD), which leverages miners to conduct useful data quality validation work instead of “useless” hash calculation. Meanwhile, in order to preserve the privacy of the sensory data, we design a homomorphic data perturbation scheme, through which miners can verify data quality without knowing the contents of the data. We have implemented a prototype of BlockSense and conducted case studies on campus, collecting over 7,000 data from workers’ mobile phones. Both simulations and real-world experiments show that BlockSense can not only improve system security, preserve data privacy and guarantee incentives fairness, but also achieve at least 5.6x faster than Ethereum smart contracts in verification efficiency. | ||
| Efficient and Non-Repudiable Data Trading Scheme Based on State Channels and Stackelberg Game | IEEE Transactions on Mobile Computing | As the Internet of Things gathers pace and popularity, more and more data is collected at the edge. To unleash the value of data and make it tradable, data markets have been proposed. However, existing data markets generally depend on broker or blockchain, which inevitably raises concerns about one or more aspects of fairness, security, or efficiency. In addition, to promote data trading in the data market, a data trading incentive mechanism is also essential. In this paper, we propose a novel data trading scheme based on state channels and Stackelberg game. First, we propose a State Channels-based Data Trading (SCDT) framework to support non-repudiable and efficient data trading. The framework can arbitrate disputes arising from off-chain data trading through state channels, enabling traders to conduct efficient transactions off-chain without worrying about security issues. Second, we propose an optimal incentive mechanism to solve the pricing and purchasing problems. The tripartite interactions among the data seller, resource seller, and user service platform are formulated as a Stackelberg game to maximize the profits of all participants. Finally, we implement the data trading framework and analyze the incentive mechanism, which reveals the feasibility of the framework and the rationality of the incentive mechanism. | ||
| A Blockchain-Empowered Incentive Mechanism for Cross-Silo Federated Learning | IEEE Transactions on Mobile Computing | In cross-silo federated learning (FL), organizations cooperatively train a global model with their local datasets. However, some organizations may act as free riders such that they only contribute a small amount of resources but can obtain a high-accuracy global model. Meanwhile, some organizations can be business competitors, and they do not trust each other or any third-party entity. In this work, our goal is to design a framework that motivates efficient cooperation among organizations without the coordination of a central entity. To this end, we propose a blockchain-empowered incentive mechanism framework for cross-silo FL. Under this incentive mechanism framework, we develop a distributed algorithm that enables organizations to achieve social efficiency, individual rationality, and budget balance without private information of the organizations. Our proposed algorithm has a proven convergence guarantee and empirically achieves a higher convergence rate than a benchmark method. Moreover, we propose a transaction minimization algorithm to reduce the number of transactions made among organizations in the blockchain. This algorithm is proven to achieve a performance no worse than twice the minimum value. The experimental results in a testbed show that our proposed framework enables organizations to achieve social efficiency within a relatively short iterative process. | ||
| A blockchain-based resource sharing incentivization mechanism for multi-to-multi in compute first networking | Comput. Networks | None | ||
| CoralDB: A Collaborative Database for Data Sharing Based on Permissioned Blockchain | IEEE Transactions on Mobile Computing | Systems that integrate distributed databases and existing blockchain platforms have recently emerged, which conveniently leverage their respective strengths to build efficient, secure, and usable data sharing and collaboration environments for different organizations. However, the performance of such systems can be limited by the native blockchain platforms due to the high latency of transactions. In this article, we present CoralDB, a bottom-up fully redesigned hybrid system of blockchain and database, aimed at enabling untrusted organizations to collaborate and share data efficiently and securely at the database level. The storage layer of CoralDB ensures data security and system throughput through key modules such as customized block structure, consensus mechanism, and transaction pool. On top of the storage layer, a database layer is introduced, which extends the blockchain of the storage layer by incorporating connection pools, collaborative tables, and query interfaces, to enhance the usability and efficiency of data collaboration and sharing. Extensive experimental results demonstrate that CoralDB provides security assurances at the level of blockchain and enables efficient decentralized data collaboration and sharing. | ||
| Digital twin: securing IoT networks using integrated ECC with blockchain for healthcare ecosystem | Knowledge and Information Systems | None | ||
| Privacy-preserving and scalable federated blockchain scheme for healthcare 4.0 | Comput. Networks | None | ||
| Mobile Blockchain-Enabled Secure and Efficient Information Management for Indoor Positioning With Federated Learning | IEEE Transactions on Mobile Computing | Traditional indoor location information management methods based on centralized servers have problems such as safe and reliable transmission, personal privacy leaks, location information tampering, and computing and storage loads. These problems have seriously affected the development of personalized services based on indoor location information. In this paper, a novel mobile blockchain-enabled federated learning (MBFL) information management framework for indoor positioning is presented, comprising the mobile blockchain model, the federated learning (FL) model, and the InterPlanetary file storage model. Then, we design the MBFL algorithm, establishing a robust foundation for collaborative model training, efficient block mining, and secure data storage. Moreover, we derive training and mining latency as well as the individual user rewards, and formulate latency-limited resource allocation strategies as a non-cooperative game. We propose an efficient alternating iterative algorithm to achieve the Nash equilibrium of this game. Numerical results demonstrate that the proposed alternating iterative algorithm achieves rapid convergence and strikes an effective balance between economic and time efficiency. Furthermore, when confronted with model poisoning attacks, the MBFL algorithm exhibits superior security performance compared to the traditional FL algorithm. Future work will focus on adapting the MBFL framework for various indoor environments and enhancing consumption and computational efficiency with hybrid consensus mechanisms. | ||
| FDSS: Flight data sharing scheme based on blockchain with dynamic, secure and efficient consensus algorithm | Comput. Networks | None | ||
| Blockchain security threats: A comprehensive classification and impact assessment | Comput. Networks | None | ||
| Practical Byzantine Fault Tolerance-Enhanced Blockchain-Enabled Data Sharing System: Latency and Age of Data Package Analysis | IEEE Transactions on Mobile Computing | Data timeliness, privacy, and security are key enablers for data-sharing systems to support time-sensitive and mission-critical systems and applications. While blockchain-enabled data sharing frameworks can offer reliable security and privacy when properly implemented, the timeliness of data and the related latency are important issues that can limit the adoption of blockchain in large-scale mission-critical applications. This paper thus carried out a performance analysis of the blockchain-enabled data-sharing framework from latency and data age perspectives to investigate the suitability of blockchain technology in data sharing systems. To achieve this, the uniqueness of such systems such as transactions validation latency, transaction generation rate, waiting time, blockchain-appending rate, and overall communication latency were jointly studied. The communication latency was characterized following the spatiotemporal modeling approach. We further adopted the practical Byzantine fault tolerance (PBFT) consensus protocol due to its well discussed suitability in large-scale data sharing applications and captured the validation stages of such a PBFT scheme using the Erlang distribution of order |
||
| Directed dynamic attribute graph anomaly detection based on evolved graph attention for blockchain | Knowledge and Information Systems | None | ||
| A blockchain-based medical IoT authentication scheme resistant to combined attacks | Comput. Networks | None | ||
| A Blockchain-Based Distributed and Intelligent Clustering-Enabled Authentication Protocol for UAV Swarms | IEEE Transactions on Mobile Computing | Unmanned aerial vehicles (UAVs) are operated remotely without the presence of a unified system of identity authentication, and wireless communications in untrusted environments can cause the loss of valuable data carried by UAVs. Traditional UAV authentication mechanisms are centralized approaches, which suffer from a single point of failure problem and may incur high complexity computations. Therefore, it is crucial to establish a distributed authentication mechanism between the ground station controller (GSC) and a UAV. Moreover, in case of UAV swarms, the high mobility of the UAVs affects the stability of UAV communications, which leads to the degradation of the UAV authentication performance. Addressing these challenges, we design a blockchain-based distributed authentication mechanism, known as SwarmAuth, for UAV swarms, where the GSC and UAVs follow a mutual authentication approach using physical unclonable functions (PUFs), and the K-means clustering-based intelligent approach is used to dynamically create location-based clusters. The blockchain helps store UAVs’ authentication information in an immutable storage and the associated smart contracts provide a convenient access control model. The security analysis of SwarmAuth is carried out through both formal and informal proofs considering general attacks. Experimental evaluation shows that SwarmAuth can assure trustworthy communications and improve the network performance. | ||
| TPE-BFL: Training Parameter E 932E ncryption scheme for Blockchain based Federated Learning system | Comput. Networks | None | ||
| BIT-FL: Blockchain-Enabled Incentivized and Secure Federated Learning Framework | IEEE Transactions on Mobile Computing | Harnessing the benefits of blockchain, such as decentralization, immutability, and transparency, to bolster the credibility and security attributes of federated learning (FL) has garnered increasing attention. However, blockchain-enabled FL (BFL) still faces several challenges. The primary and most significant issue arises from its essential but slow validation procedure, which selects high-quality local models by recruiting distributed validators. The second issue stems from its incentive mechanism under the transparent nature of blockchain, increasing the risk of privacy breaches regarding workers’ cost information. The final challenge involves data eavesdropping from shared local models. To address these significant obstacles, this paper proposes a Blockchain-enabled Incentivized and Secure Federated Learning (BIT-FL) framework. BIT-FL leverages a novel loop-based sharded consensus algorithm to accelerate the validation procedure, ensuring the same security as non-sharded consensus protocols. It consistently outputs the correct local model selection when the fraction of adversaries among validators is less than $1/2$mml:mathmml:mrowmml:mn1</mml:mn>mml:mo/</mml:mo>mml:mn2</mml:mn></mml:mrow></mml:math> with synchronous communication. Furthermore, BIT-FL integrates a randomized incentive procedure, attracting more participants while guaranteeing the privacy of their cost information through meticulous worker selection probability design. Finally, by adding artificial Gaussian noise to local models, it ensures the privacy of trainers’ local models. With the careful design of Gaussian noise, the excess empirical risk of BIT-FL is upper-bounded by $\mathcal {O}(\frac{\ln n_{\min}}{ n_{\min}^{3/2}}+\frac{\ln n}{n})$mml:mathmml:mrow<mml:mi mathvariant="script">O</mml:mi>mml:mo(</mml:mo>mml:mfracmml:mrow<mml:mo form="prefix">ln</mml:mo>mml:msubmml:min</mml:mi><mml:mo movablelimits="true" form="prefix">min</mml:mo></mml:msub></mml:mrow>mml:msubsupmml:min</mml:mi>mml:mrow<mml:mo movablelimits="true" form="prefix">min</mml:mo></mml:mrow>mml:mrowmml:mn3</mml:mn>mml:mo/</mml:mo>mml:mn2</mml:mn></mml:mrow></mml:msubsup></mml:mfrac>mml:mo+</mml:mo>mml:mfracmml:mrow<mml:mo form="prefix">ln</mml:mo>mml:min</mml:mi></mml:mrow>mml:min</mml:mi></mml:mfrac>mml:mo)</mml:mo></mml:mrow></mml:math>, where $n$mml:mathmml:min</mml:mi></mml:math> represents the size of the union dataset, and |
||
| RIC-SDA: A Reputation Incentive Committee-Based Secure Conditional Dual Authentication Scheme for VANETs | IEEE Transactions on Mobile Computing | Vehicular ad hoc networks (VANETs) establish wireless connections among all vehicles, enabling seamless mobile communication. However, existing conditional privacy protection VANETs authentication schemes fail to address the issue of potential key-exposure and do not provide accelerated vehicle authentication. In this paper, we propose a reputation incentive committee-based secure conditional dual authentication scheme for VANETs called RIC-SDA. Our proposed scheme incorporates dual authentication of the consensus committee and vehicle-to-vehicle (V2V) communication. It enables the rapid provision of dynamic vehicle epoch-key from consensus committee authentication for V2V authentication through our designed reputation incentive mechanism. To mitigate the potential key-exposure problem, we introduce a novel concept of secure vehicle epoch communication, which means V2V authentication is valid for only one epoch blockchain unit time. The proposed scheme achieves lightweight computation and incurs minimal communication overheads, with the signature size being just 137 bytes. The RIC-SDA scheme supports fast batch verification. We prove that our proposed scheme is unforgeable security under random oracle and demonstrate its feasibility by implementing it in a test network based on Ethereum Sepolia. The results demonstrate that our RIC-SDA solution outperforms the existing state-of-the-art authentication VANET schemes regarding efficiency and communication costs. | ||
| ArchW3: An adaptive blockchain wallet architecture for Web3 applications | Comput. Networks | None | ||
| Predicting IoT Distributed Ledger Fraud Transactions With a Lightweight GAN Network | IEEE Transactions on Mobile Computing | Decision-making and consensus in traditional blockchain protocols is formulated as a repeated Bernoulli trial that solves a computationally-intense lottery puzzle, called Proof-of-Work (PoW) in Bitcoin. This approach has shown robustness through practice, but does not scale with increasing network size and generation of new transactions. Resource constrained Internet of Things (IoT) networks are incompatible with full computation of schemes like Bitcoin's PoW. Our effort proposes a first step towards an alternative consensus using machine learning-based decision-making with prediction of fraud transactions to alleviate need for intense computation. To improve base approval probabilities for fraud detection in an ideal security setting, Vector GAN (VecGAN) is proposed to augment blockchain data in classifier training, which combines error-driven learning with Bayesian estimation to alleviate calculations. This two-step approach with augmentation and classification on new transactions is proposed as a novel approach to blockchain decision-making. Experimental prediction accuracy using VecGAN improved up to 3% on simplistic classifiers compared to other state-of-the-art augmentation techniques. Resource consumption in a realistic blockchain setting was reduced while improving block throughput by 50% compared to PoW. Future work will explore Sybil-spam defensive measures for realistic protocol implementation with this approach. | ||
| Adaptive Incentive and Resource Allocation for Blockchain-Supported Edge Video Streaming Systems: A Cooperative Learning Approach | IEEE Transactions on Mobile Computing | Edge computing significantly enhanced the growth of edge-assistant video streaming applications. However, challenges such as unpredictable wireless conditions, resource constraints, and task redundancy have intertwined impacts on the overall performance of edge video streaming systems (EVS). Therefore, it is essential to have an integrated framework that addresses resource management, computational offloading, and video task preprocessing. Existing optimization strategies often neglect the simultaneous management of computational offloading, resource allocation, and video task preprocessing, leading to a suboptimal system utility. Moreover, they struggle to handle high-dimensional decision variables. On the other hand, learning-based adaptive schemes fall short in integrating distributed decisions and ensuring the scalability of wireless devices. Additionally, current approaches lack adaptive incentives. To bridge these gaps, we propose a novel framework called AIRA, which is based on improved multi-agent reinforcement learning (MARL) and smart contracts. AIRA manages resources, video compression, and adaptive incentives in a distributed manner. It consists of a MARL-driven cooperative learning algorithm (CLA) and a smart contract-guided adaptive incentive mechanism. Leveraging an actor-critic structure, the CLA enables wireless devices to master strategies for resource allocation, video task compression, and offloading, utilizing historical data. Notably, the CLA incorporates an attention mechanism to select pivotal tuples from the observation-action pairings among different agents, ensuring improved scalability and computational prowess. Evaluations based on real-world trajectories demonstrate that AIRA enables adaptive incentives. Compared to state-of-the-art approaches, CLA effectively enhances the long-term system utility and scalability of EVS. | ||
| Decentralized traffic detection utilizing blockchain-federated learning with quality-driven aggregation | Comput. Networks | None | ||
| HTFabric: A Fast Re-ordering and Parallel Re-execution Method for a High-Throughput Blockchain | International Conference on Information and Knowledge Management | None | ||
| Blockchain-assisted Verifiable Secure Multi-Party Data Computing | Comput. Networks | None | ||
| A Vehicular Trust Blockchain Framework With Scalable Byzantine Consensus | IEEE Transactions on Mobile Computing | The maturing blockchain technology has gradually promoted decentralized data storage from cryptocurrencies to other applications, such as trust management, resulting in new challenges based on specific scenarios. Taking the mobile trust blockchain within a vehicular network as an example, many users require the system to process massive traffic information for accurate trust assessment, preserve data reliably, and respond quickly. While existing vehicular blockchain systems ensure immutability, transparency, and traceability, they are limited in terms of scalability, performance, and security. To address these issues, this paper proposes a novel decentralized vehicle trust management solution and a well-matched blockchain framework that provides both security and performance. The paper primarily addresses two issues: i) To provide accurate trust evaluation, the trust model adopts a decentralized and peer-review-based trust computation method secured by trusted execution environments (TEEs). ii) To ensure reliable trust management, a multi-shard blockchain framework is developed with a novel hierarchical Byzantine consensus protocol, improving efficiency and security while providing high scalability and performance. The proposed scheme combines the decentralized trust model with a multi-shard blockchain, preserving trust information through a hierarchical consensus protocol. Finally, real-world experiments are conducted by developing a testbed deployed on both local and cloud servers for performance measurements. | ||
| Multi-Modal Federated Learning Based Resources Convergence for Satellite-Ground Twin Networks | IEEE Transactions on Mobile Computing | Satellite-ground twin networks (SGTNs) are regarded as a promising service paradigm, which can provide mega access services and powerful computation offloading capabilities via cloud-fog automation functions. Specifically, cloud-fog automation technologies are collaboratively leveraged to enable dense connectivity, pervasive computing, and intelligent control in terrestrial industrial cyber-physical systems, whose system-level privacy security can be strengthened via blockchain based consensus protocol. Moreover, digital twin (DT) can shorten the gap between physical unities and digital space to enable instant data mapping in SGTNs environments. However, complex multi-modal network environments, such as stochastic task size, dynamic low earth orbit location, and time-varying channel gains, hinder better performance metrics in terms of energy consumption, throughput and privacy overhead. Hence, we establish a SGTN integrated cloud-fog automation model to transfer task data to low earth orbit satellites, and then execute broad communication access, powerful computation offloading, and efficient twin control. Next, we propose a Lyapunov stability theory based multi-modal federated learning (LST-MMFL) method to optimize the battery energy, the size of block, computation frequency, and the number of twin control for minimizing the total energy consumption and privacy overhead. Furthermore, we design a novel blockchain based transaction verification protocol to strengthen privacy security, derive performance upper bounds of SGTN model, and fulfill the long-term average task as well as energy queue constraints. Finally, massive simulation results show that the proposed LST-MMFL algorithm outperforms existing state-of-the-art benchmarks in line with energy consumption, available battery level, networked control and privacy protection overhead. | ||
| BEET: Blockchain Enabled Energy Trading for E-Mobility Oriented Electric Vehicles | IEEE Transactions on Mobile Computing | Renewable Energy Sources (RESs) are gaining considerable attention to reduce human dependence on fossil fuels and minimize harmful gases in our surroundings. Existing literature on energy trading focused on providing renewable energy to smart homes, smart buildings, and smart offices to fulfill their daily energy demands obtained from RESs. Besides, Electric Vehicles (EVs) use either power grid energy or a battery exchange mechanism to recharge their low EV batteries. The continuous use of power grids to recharge low EV batteries causes a significant load on power grids. Due to this, power grids are inadequate to fulfill the ever-increasing demands of EVs in the future. In this context, we propose a Blockchain Enabled Energy Trading (BEET) framework oriented EV charging. A system architecture of the BEET framework is presented to describe the functioning of each layer and its associated entities. We formulate an optimization problem that maximizes the revenue in the energy trading process using a knapsack optimization. Smart contracts are designed on the consortium blockchain network to sell and buy renewable energy to aggregators and from producers, respectively. Moreover, an EV charging mechanism is designed to intelligently allocate renewable energy to consumers at a low price. A comparative analysis is performed with state-of-the-art works in terms of charging price, revenue, throughput, and latency. The results indicate that the BEET framework outperforms compared to state-of-the-art works to address the renewable energy demand problem to realize E-mobility. It is clarified that the data considered in the experimental analysis were obtained from statistical simulations in realistic E-Mobility environment settings. |