Enhancing IoT Data Security with Lightweight Blockchain and Okamoto Uchiyama Homomorphic Encryption

Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges.Concurrently,the Internet of Things(IoT)has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services,ultimately enhancing human lives.This paper presents a new approach utilizing lightweight blockchain technology,effectively reducing the computational burden typically associated with conventional blockchain systems.By integrating this lightweight blockchain with IoT systems,substantial reductions in implementation time and computational complexity can be achieved.Moreover,the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm,renowned for its homomorphic characteristics,to reinforce the privacy and security of IoT-generated data.The integration of homomorphic encryption and blockchain technology establishes a secure and decentralized platformfor storing and analyzing sensitive data of the supply chain data.This platformfacilitates the development of some business models and empowers decentralized applications to perform computations on encrypted data while maintaining data privacy.The results validate the robust security of the proposed system,comparable to standard blockchain implementations,leveraging the distinctive homomorphic attributes of the Okamoto Uchiyama algorithm and the lightweight blockchain paradigm.

A Blockchain-Based Access Control Scheme for Reputation Value Attributes of the Internet of Things

The Internet of Things(IoT)access controlmechanism may encounter security issues such as single point of failure and data tampering.To address these issues,a blockchain-based IoT reputation value attribute access control scheme is proposed.Firstly,writing the reputation value as an attribute into the access control policy,and then deploying the access control policy in the smart contract of the blockchain system can enable the system to provide more fine-grained access control;Secondly,storing a large amount of resources fromthe Internet of Things in Inter Planetary File System(IPFS)to improve system throughput;Finally,map resource access operations to qualification tokens to improve the performance of the access control system.Complete simulation experiments based on the Hyperledger Fabric platform.Fromthe simulation experimental results,it can be seen that the access control system can achieve more fine-grained and dynamic access control while maintaining high throughput and low time delay,providing sufficient reliability and security for access control of IoT devices.

Data Secure Storage Mechanism for IIoT Based on Blockchain

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of IIoT.Blockchain technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the IIoT.In the traditional blockchain,data is stored in a Merkle tree.As data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based IIoT.Accordingly,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data.To solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC structure.Secondly,this paper uses PVC instead of the Merkle tree to store big data generated by IIoT.PVC can improve the efficiency of traditional VC in the process of commitment and opening.Finally,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments.This mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

Crowdsourcing holds broad applications in information acquisition and dissemination,yet encounters challenges pertaining to data quality assessment and user reputation management.Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores,thereby elevating the quality and dependability of crowdsourced data.However,these mechanisms face several challenges in traditional crowdsourcing systems:1)platform security lacks robust guarantees and may be susceptible to attacks;2)there exists a potential for large-scale privacy breaches;and 3)incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations,occasionally lacking a dedicated reputation update module.This paper introduces a reputation update scheme tailored for crowdsourcing,with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing system.Here,the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method(ECOD).Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and tamper-resistance.Computation of user reputation scores relies on their behavioral history,actively discouraging undesirable conduct.Additionally,we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation,enabling the system to allocate incentives based on user behavior and reputation.Our scheme undergoes evaluation on 11 datasets,revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious behavior.This research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications,holding promise for fostering more reliable and high-quality data collection in crowdsourcing across diverse domains.

A Survey on Blockchain-Based Federated Learning:Categorization,Application and Analysis

Federated Learning(FL),as an emergent paradigm in privacy-preserving machine learning,has garnered significant interest from scholars and engineers across both academic and industrial spheres.Despite its innovative approach to model training across distributed networks,FL has its vulnerabilities;the centralized server-client architecture introduces risks of single-point failures.Moreover,the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors.Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals.For these reasons,some participants unwilling use their private data to train a model,which is a bottleneck in the development and industrialization of federated learning.Blockchain technology,characterized by its decentralized ledger system,offers a compelling solution to these issues.It inherently prevents single-point failures and,through its incentive mechanisms,motivates participants to contribute computing power.Thus,blockchain-based FL(BCFL)emerges as a natural progression to address FL’s challenges.This study begins with concise introductions to federated learning and blockchain technologies,followed by a formal analysis of the specific problems that FL encounters.It discusses the challenges of combining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during communication and incentives in BCFL.In addition,this research examines the use of BCFL in various fields,such as the Internet of Things and the Internet of Vehicles.Finally,it assesses the effectiveness of these solutions.

A Framework for Enhancing Privacy and Anonymity in Blockchain-Enabled IoT Devices

With the increase in IoT(Internet of Things)devices comes an inherent challenge of security.In the world today,privacy is the prime concern of every individual.Preserving one’s privacy and keeping anonymity throughout the system is a desired functionality that does not come without inevitable trade-offs like scalability and increased complexity and is always exceedingly difficult to manage.The challenge is keeping confidentiality and continuing to make the person innominate throughout the system.To address this,we present our proposed architecture where we manage IoT devices using blockchain technology.Our proposed architecture works on and off blockchain integrated with the closed-circuit television(CCTV)security camera fixed at the rental property.In this framework,the CCTV security camera feed is redirected towards the owner and renter based on the smart contract conditions.One entity(owner or renter)can see the CCTV security camera feed at one time.There is no third-party dependence except for the CCTV security camera deployment phase.Our contributions include the proposition of framework architecture,a novel smart contract algorithm,and the modification to the ring signatures leveraging an existing cryptographic technique.Analyses are made based on different systems’security and key management areas.In an empirical study,our proposed algorithm performed better in key generation,proof generation,and verification times.By comparing similar existing schemes,we have shown the proposed architectures’advantages.Until now,we have developed this system for a specific area in the real world.However,this system is scalable and applicable to other areas like healthcare monitoring systems,which is part of our future work.

A Blockchain and CP-ABE Based Access Control Scheme with Fine-Grained Revocation of Attributes in Cloud Health

The Access control scheme is an effective method to protect user data privacy.The access control scheme based on blockchain and ciphertext policy attribute encryption(CP–ABE)can solve the problems of single—point of failure and lack of trust in the centralized system.However,it also brings new problems to the health information in the cloud storage environment,such as attribute leakage,low consensus efficiency,complex permission updates,and so on.This paper proposes an access control scheme with fine-grained attribute revocation,keyword search,and traceability of the attribute private key distribution process.Blockchain technology tracks the authorization of attribute private keys.The credit scoring method improves the Raft protocol in consensus efficiency.Besides,the interplanetary file system(IPFS)addresses the capacity deficit of blockchain.Under the premise of hiding policy,the research proposes a fine-grained access control method based on users,user attributes,and file structure.It optimizes the data-sharing mode.At the same time,Proxy Re-Encryption(PRE)technology is used to update the access rights.The proposed scheme proved to be secure.Comparative analysis and experimental results show that the proposed scheme has higher efficiency and more functions.It can meet the needs of medical institutions.

A Blockchain-Based Game Approach to Multi-Microgrid Energy Dispatch

As the current global environment is deteriorating,distributed renewable energy is gradually becoming an important member of the energy internet.Blockchain,as a decentralized distributed ledger with decentralization,traceability and tamper-proof features,is an importantway to achieve efficient consumption andmulti-party supply of new energy.In this article,we establish a blockchain-based mathematical model of multiple microgrids and microgrid aggregators’revenue,consider the degree of microgrid users’preference for electricity thus increasing users’reliance on the blockchainmarket,and apply the one-master-multiple-slave Stackelberg game theory to solve the energy dispatching strategy when each market entity pursues the maximum revenue.The simulation results show that the blockchain-based dynamic game of the multi-microgrid market can effectively increase the revenue of both microgrids and aggregators and improve the utilization of renewable energy.

On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault tolerance.However,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal mechanism.As a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain network.To address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional PoA.This paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided consensus.First,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart contracts.Second,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing throughput.In particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation variation.Moreover,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s liveness.Finally,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

Automated Vulnerability Detection of Blockchain Smart Contacts Based on BERT Artificial Intelligent Model

The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.

Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network:A Comprehensive Survey

Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance,transportation,healthcare,education,and supply chain management.Blockchain interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application exchanges.However,the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain nodes.There is the biggest challenge of data integrity and scalability,including significant computing complexity and inapplicable latency on regional network diversity,operating system diversity,bandwidth diversity,node diversity,etc.,for decision-making of data transactions across blockchain-based heterogeneous networks.Data security and privacy have also become the main concerns across the heterogeneous network to build smart IoT ecosystems.To address these issues,today’s researchers have explored the potential solutions of the capability of heterogeneous network devices to perform data transactions where the system stimulates their integration reliably and securely with blockchain.The key goal of this paper is to conduct a state-of-the-art and comprehensive survey on cybersecurity enhancement using blockchain in the heterogeneous network.This paper proposes a full-fledged taxonomy to identify the main obstacles,research gaps,future research directions,effective solutions,andmost relevant blockchain-enabled cybersecurity systems.In addition,Blockchain based heterogeneous network framework with cybersecurity is proposed in this paper tomeet the goal of maintaining optimal performance data transactions among organizations.Overall,this paper provides an in-depth description based on the critical analysis to overcome the existing work gaps for future research where it presents a potential cybersecurity design with key requirements of blockchain across a heterogeneous network.

For Mega-Constellations: Edge Computing and Safety Management Based on Blockchain Technology

In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure communication.While edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.

A Sharding Scheme Based on Graph Partitioning Algorithm for Public Blockchain

Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business processes.However,transaction performance and scalability has become the main challenges hindering the widespread adoption of blockchain.Due to its inability to meet the demands of high-frequency trading,blockchain cannot be adopted in many scenarios.To improve the transaction capacity,researchers have proposed some on-chain scaling technologies,including lightning networks,directed acyclic graph technology,state channels,and shardingmechanisms,inwhich sharding emerges as a potential scaling technology.Nevertheless,excessive cross-shard transactions and uneven shard workloads prevent the sharding mechanism from achieving the expected aim.This paper proposes a graphbased sharding scheme for public blockchain to efficiently balance the transaction distribution.Bymitigating crossshard transactions and evening-out workloads among shards,the scheme reduces transaction confirmation latency and enhances the transaction capacity of the blockchain.Therefore,the scheme can achieve a high-frequency transaction as well as a better blockchain scalability.Experiments results show that the scheme effectively reduces the cross-shard transaction ratio to a range of 35%-56%and significantly decreases the transaction confirmation latency to 6 s in a blockchain with no more than 25 shards.

Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

A General Framework for Intelligent IoT Data Acquisition and Sharing in an Untrusted Environment Based on Blockchain

Traditional Io T systems suffer from high equipment management costs and difficulty in trustworthy data sharing caused by centralization.Blockchain provides a feasible research direction to solve these problems. The main challenge at this stage is to integrate the blockchain from the resourceconstrained Io T devices and ensure the data of Io T system is credible. We provide a general framework for intelligent Io T data acquisition and sharing in an untrusted environment based on the blockchain, where gateways become Oracles. A distributed Oracle network based on Byzantine Fault Tolerant algorithm is used to provide trusted data for the blockchain to make intelligent Io T data trustworthy. An aggregation contract is deployed to collect data from various Oracle and share the credible data to all on-chain users. We also propose a gateway data aggregation scheme based on the REST API event publishing/subscribing mechanism which uses SQL to achieve flexible data aggregation. The experimental results show that the proposed scheme can alleviate the problem of limited performance of Io T equipment, make data reliable, and meet the diverse data needs on the chain.

VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

High-Level Security Dimension Evaluation of Blockchain BaaS System and Key Technology

In recent years,blockchain technology integration and application has gradually become an important driving force for new technological innovation and industrial transformation.While blockchain technology and applications are developing rapidly,the emerging security risks and obstacles have gradually become prominent.Attackers can still find security issues in blockchain systems and conduct attacks,causing increasing losses from network attacks every year.In response to the current demand for blockchain application security detection and assessment in all industries,and the insufficient coverage of existing detection technologies such as smart contract detectiontechnology,this paper proposes a blockchain core technology security assessment system model,and studies the relevant detection and assessment key technologies and systems.A security assessment scheme based on a smart contract and consensus mechanism detection scheme is designed.And the underlying blockchain architecture supports the traceability of detection results using super blockchains.Finally,the functionality and performance of the system were tested,and the test results show that the model and solutions proposed in this paper have good feasibility.

An Encode-and CRT-Based Scalability Scheme for Optimizing Transmission in Blockchain

Blockchain technology has witnessed a burgeoning integration into diverse realms of economic and societal development.Nevertheless,scalability challenges,characterized by diminished broadcast efficiency,heightened communication overhead,and escalated storage costs,have significantly constrained the broad-scale application of blockchain.This paper introduces a novel Encode-and CRT-based Scalability Scheme(ECSS),meticulously refined to enhance both block broadcasting and storage.Primarily,ECSS categorizes nodes into distinct domains,thereby reducing the network diameter and augmenting transmission efficiency.Secondly,ECSS streamlines block transmission through a compact block protocol and robust RS coding,which not only reduces the size of broadcasted blocks but also ensures transmission reliability.Finally,ECSS utilizes the Chinese remainder theorem,designating the block body as the compression target and mapping it to multiple modules to achieve efficient storage,thereby alleviating the storage burdens on nodes.To evaluate ECSS’s performance,we established an experimental platformand conducted comprehensive assessments.Empirical results demonstrate that ECSS attains superior network scalability and stability,reducing communication overhead by an impressive 72% and total storage costs by a substantial 63.6%.

CRBFT:A Byzantine Fault-Tolerant Consensus Protocol Based on Collaborative Filtering Recommendation for Blockchains

Blockchain has been widely used in finance,the Internet of Things(IoT),supply chains,and other scenarios as a revolutionary technology.Consensus protocol plays a vital role in blockchain,which helps all participants to maintain the storage state consistently.However,with the improvement of network environment complexity and system scale,blockchain development is limited by the performance,security,and scalability of the consensus protocol.To address this problem,this paper introduces the collaborative filtering mechanism commonly used in the recommendation system into the Practical Byzantine Fault Tolerance(PBFT)and proposes a Byzantine fault-tolerant(BFT)consensus protocol based on collaborative filtering recommendation(CRBFT).Specifically,an improved collaborative filtering recommendation method is designed to use the similarity between a node’s recommendation opinions and those of the recommender as a basis for determining whether to adopt the recommendation opinions.This can amplify the recommendation voice of good nodes,weaken the impact of cunningmalicious nodes on the trust value calculation,andmake the calculated resultsmore accurate.In addition,the nodes are given voting power according to their trust value,and a weight randomelection algorithm is designed and implemented to reduce the risk of attack.The experimental results show that CRBFT can effectively eliminate various malicious nodes and improve the performance of blockchain systems in complex network environments,and the feasibility of CRBFT is also proven by theoretical analysis.

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingthe significance of using cutting-edge technology to meet rising privacy issues in digital identity.