Blockchain Papers

Hangyun Tang, Yanan Qiao, Fan Yang, Bowen Cai · 5 authors

No abstract is available for this record.

Dajun Zhang, Wei Shi, Marc St‐Hilaire, Ruizhe Yang

Recently, Internet of Vehicles (IoV) and Machine Learning (ML) have attracted more and more attention. Considering inefficient real-time training and high requirements on computing capabilities of centralized data collection, performing Distributed Machine Learning (DML) in IoV has become an important research branch. However, the heterogeneity, mobility, and distrust among IoV nodes affect how to execute DML effectively, securely, and in a salable manner. In this paper, a blockchain-based Cooperative Learning framework combined with a Deep Compression method (CLDC) is proposed. First, we improve the local training efficiency of lightweight IoV nodes by using deep compression method. Meanwhile, we have introduced a blockchain system in CLDC, the significance of which is that we have completed the transformation from centralized architecture to distributed framework through the blockchain, and shared local training results in a verifiable manner. The framework uses non-tamperable features of the blockchain to ensure the security of local training results. Moreover, we propose a Learning-based Redundant Byzantine Fault Tolerance (L-RBFT) protocol, in which the primary node needs to confirm the loss percentage of learning in the transaction before forwarding the RBFT messages. The significance of L-RBFT is to ensure that IoV nodes obtain the best training results through the consensus of blockchain nodes. We use it to solve the computing and communication resource allocation problem in IoV to clarify the operating mechanism of the proposed framework. The experimental results prove that this scheme performs better when compared with the traditional centralized deep reinforcement learning method.

A S Manjunatha, S Arpith, G M Mufeed, K R Anusha · 5 authors

In the current healthcare environment, they lock the patient records in multiple centralized systems which are maintained by the different healthcare institutions. So, the complete, comprehensive medical data history of the patient is locked away, making it difficult for doctors to make informed decisions. Our system aims at tackling these issues using a decentralized system to store the patient's record. The patient and the doctor/healthcare institutions use a Decentralized Application as an interface to the Blockchain network. When a patient visits the doctor, the patient can give access to his/her medical data through this Decentralized Application via an Ethereum smart contract. Once the patient gives access, the doctor can access all the patient's medical records and history in one unified interface. Artificial Intelligence and Machine Learning are used to give a tailored medical experience to the patients. With rich data that is available from the users' network, can be fed into the Machine Learning models to do various levels of analysis to give patients and doctors further insight into the medical records.

Zahra Ghodsi, Mojan Javaheripi, Nojan Sheybani, Xinqiao Zhang · 6 authors

Privacy-preserving federated learning allows multiple users to jointly train a model with coordination of a central server. The server only learns the final aggregation result, thereby preventing leakage of the users’ (private) training data from the individual model updates. However, keeping the individual updates private allows malicious users to degrade the model accuracy without being detected, also known as Byzantine attacks. Best existing defenses against Byzantine workers rely on robust rank-based statistics, e.g., setting robust bounds via the median of updates, to find malicious updates. However, implementing privacy-preserving rank-based statistics, especially median-based, is nontrivial and unscalable in the secure domain, as it requires sorting of all individual updates. We establish the first private robustness check that uses high break point rank-based statistics on aggregated model updates. By exploiting randomized clustering, we significantly improve the scalability of our defense without compromising privacy. We leverage the derived statistical bounds in zero-knowledge proofs to detect and remove malicious updates without revealing the private user updates. Our novel framework, zPROBE, enables Byzantine resilient and secure federated learning. We show the effectiveness of zPROBE on several computer vision benchmarks. Empirical evaluations demonstrate that zPROBE provides a low overhead solution to defend against state-of-the-art Byzantine attacks while preserving privacy.

Jonathan Heiss, Elias Grünewald, Stefan Tai, Nikolas Haimerl · 5 authors

Federated learning may be subject to both global aggregation attacks and distributed poisoning attacks. Blockchain technology along with incentive and penalty mechanisms have been suggested to counter these. In this paper, we explore verifiable off-chain computations using zero-knowledge proofs as an alternative to incentive and penalty mechanisms in blockchain-based federated learning. In our solution, learning nodes, in addition to their computational duties, act as off-chain provers submitting proofs to attest computational correctness of param-eters that can be verified on the blockchain. We demonstrate and evaluate our solution through a health monitoring use case and proof-of-concept implementation leveraging the ZoKrates language and tools for smart contract-based on-chain model management. Our research introduces verifiability of correctness of learning processes, thus advancing blockchain-based federated learning.

Weize Xu, Hongyue Duan, Xiao Chen, Jie Huang · 6 authors

Abstract With the boom in 5G technology, mobile spatial crowdsourcing has shown great dynamism in industrial mobile communications and edge computing node management. But the traditional crowdsourcing system is not advanced enough to adapt to the new environment. Typically, traditional crowdsourcing workflow is hosted by a centralized crowdsourcing platform. However, the centralized crowdsourcing platform faces the following problems: (1) single point of failure, (2) user privacy leakage, (3) subjective arbitration, (4) additional service fee, and (5) non-transparent task assignment process. To improve those problems, we replaced the centralized crowdsourcing platform with a decentralized blockchain infrastructure. And we analyzed the challenge problems of multi-skilled spatial crowdsourcing tasks in the blockchain crowdsourcing system. In addition, a crowdsourcing task allocation algorithm has been proposed, which implements a transparent task distribution process and can adapt to the computing-constrained environment on the blockchain. Compared with the TSWCrowd blockchain-based crowdsourcing model, our system has a higher task allocation rate under the same conditions. And the experimental result shows our work has good economic feasibility, which decentralizes the crowdsourcing process and significantly reduces the additional consumption of the crowdsourcing process.

Shuangjie Bai, Geng Yang, Guoxiu Liu, Hua Dai · 5 authors

In federated learning, multiple parties may use their data to cooperatively train a model without exchanging raw data. Federated learning protects the privacy of users to a certain extent. However, model parameters may still expose private information. Moreover, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsuitable for federated learning and vulnerable to security risks. To mitigate these issues, we propose a privacy-preserving oriented no trusted third party federated learning system based on blockchain (NttpFL). The initiator of the federated learning task and the partners negotiate keys through the conference key agreement and do not need to distribute keys through a trusted third party. We design a double-layer encryption mechanism to ensure privacy. Partners cannot obtain any private information other than their information. The decentralized nature of blockchain suits our system. In addition, blockchain makes the entire process transparent and traceable and avoids the single node failure problem. Experimental results confirm that the proposed method significantly reduces the communication costs and computational complexity compared to existing encrypted federated learning without compromising the performance and security.

Mohammad Saidur Rahman, Abdulatif Alabdulatif, Ibrahim Khalil

No abstract is available for this record.

Dinh C. Nguyen, Van‐Dinh Nguyen, Ming Ding, Symeon Chatzinotas · 8 authors

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things (IoT) networks, enabling task offloading with security enhancement based on blockchain mining. Yet the existing approaches for these enabling technologies are isolated, providing only tailored solutions for specific services and scenarios. To fill this gap, we propose a novel cooperative task offloading and blockchain mining (TOBM) scheme for a blockchain-based MEC system, where each edge device not only handles computation tasks but also conducts block mining for improving system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. To accommodate the highly dynamic environment and high-dimensional system state space, we apply a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. Experimental results demonstrate the superior performance of the proposed TOBM scheme in terms of enhanced system reward, improved offloading utility with lower blockchain mining latency, and better system utility, compared to the existing cooperative and non-cooperative schemes. The article concludes with key technical challenges and possible directions for future blockchain-based MEC research.

R. Durga, E. Poovammal

With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.

Pardeep Kumar, A. Suresh, V. Anbarasu, S. P. Anandaraj · 5 authors

No abstract is available for this record.

Surarapu Sunitha, Nampalli Shirisha, Batchu Teja Sai Satish, Koyalakonda Vishnu · 5 authors

In this paper, we present a model of a multi-client framework for access control to datasets put away in an untrusted cloud climate. Distributed storage like some other untrusted climate needs the capacity to get share data. Our methodology gives an entrance command over the information put away in the cloud the supplier investment. The fundamental device of the access control instrument is a ciphertext-strategy trait-based encryption plot with dynamic credits. Utilizing a blockchain-based decentralized record, our framework gives a permanent log of all significant security occasions, for example, key age, access strategy task, change or repudiation, and access demand. We propose a bunch of cryptographic conventions guaranteeing the security of cryptographic tasks requiring mystery or private keys. Just ciphertexts of hash codes are moved through the blockchain record. The model of our framework is executed utilizing shrewd agreements and tried on the Ethereum blockchain stage. Keywords- cloud storage; attribute-based access control; ciphertext-policy attribute-based encryption; blockchain

Gianluca Lax, Antonia Russo

Location-Based Services (LBSs) are essential in many application contexts like ride-sharing or navigation apps. There are cases where users could gain an advantage by submitting fake locations. The problem faced in this paper concerns the possibility that the geographic location declared by a user is not the actual location in which the user is placed. Some solutions are based on centralized or distributed verification in the literature, and other solutions are based on witnesses or infrastructure. In this paper, we highlight the limitations of such approaches and propose a new scheme that exploits signals coming from satellites to provide trustworthy location proofs, also respecting users' privacy. The proposed approach is decentralized because location proofs are stored by users in a suitably-encrypted way, and a blockchain is adopted to guarantee data integrity and authenticity. We show that the proposed approach overcomes the state of the art through a detailed analysis.

Jiawen Kang, Dongdong Ye, Jiangtian Nie, Jiang Xiao · 9 authors

The emerging industrial metaverses realize the map-ping and expanding operations of physical industry into virtual space for significantly upgrading intelligent manufacturing. The industrial metaverses obtain data from various production and operation lines by Industrial Internet of Things (IIoT), and thus conduct effective data analysis and decision-making, thereby en-hancing the production efficiency of the physical space, reducing operating costs, and maximizing commercial value. However, there still exist bottlenecks when integrating metaverses into IIoT, such as the privacy leakage of sensitive data with commercial secrets, IIoT sensing data freshness, and incentives for sharing these data. In this paper, we design a user-defined privacy-preserving framework with decentralized federated learning for the industrial metaverses. To further improve privacy protection of industrial metaverse, a cross-chain empowered federated learning framework is further utilized to perform decentralized, secure, and privacy-preserving data training on both physical and virtual spaces through a hierarchical blockchain architecture with a main chain and multiple subchains. Moreover, we introduce the age of information as the data freshness metric and thus design an age-based contract model to motivate data sensing among IIoT nodes. Numerical results indicate the efficiency of the proposed framework and incentive mechanism in the industrial metaverses.

Songling Chen, Xiangling Fu, Hongchao Si, Yi Wang · 6 authors

Abstract Health Internet of Things (Health IoT) has been limited by isolated information and a lack of security. As the combination of blockchain and Health IoT could potentially address these two limitations, it has attracted significant interest. However, blockchain‐based systems often fail to balance data sharing and privacy protection. Therefore, we proposed a Health IoT‐based privacy‐preserving data sharing blockchain system. We designed a privacy‐preserving method based on the content extraction signature scheme to enable patients to establish fine‐grained privacy protection. We designed a Byzantine fault‐tolerant leader election mechanism that enhances the security of the Raft algorithm while providing efficiency in the data sharing. Furthermore, we designed a summary contract to ensure efficient data retrieval. The proposed mechanism was evaluated in terms of the efficiency and security. The simulation and analysis results demonstrate that our scheme offers a secure and effective technique for achieving privacy‐preserving and efficient sharing of IoT medical data.

Gonzalo Munilla Garrido, Johannes Sedlmeir, Matthias Babel

Analyses that fulfill differential privacy provide plausible deniability to individuals while allowing analysts to extract insights from data. However, beyond an often acceptable accuracy tradeoff, these statistical disclosure techniques generally inhibit the verifiability of the provided information, as one cannot check the correctness of the participants' truthful information, the differentially private mechanism, or the unbiased random number generation. While related work has already discussed this opportunity, an efficient implementation with a precise bound on errors and corresponding proofs of the differential privacy property is so far missing. In this paper, we follow an approach based on zero-knowledge proofs~(ZKPs), in specific succinct non-interactive arguments of knowledge, as a verifiable computation technique to prove the correctness of a differentially private query output. In particular, we ensure the guarantees of differential privacy hold despite the limitations of ZKPs that operate on finite fields and have limited branching capabilities. We demonstrate that our approach has practical performance and discuss how practitioners could employ our primitives to verifiably query individuals' age from their digitally signed ID card in a differentially private manner.

Jingwei Liu, Weiyang Jiang, Rong Sun, Ali Kashif Bashir · 7 authors

As an important carrier of healthcare data, Electronic Medical Records (EMRs) generated from various sensors, i.e., wearable, implantable, are extremely valuable research materials for artificial intelligence and machine learning. The efficient circulation of EMRs can improve remote medical services and promote the development of the related healthcare industry. However, in traditional centralized data sharing architectures, the balance between privacy and traceability still cannot be well handled. To address the issue that malicious users cannot be locked in the fully anonymous sharing schemes, we propose a trackable anonymous remote healthcare data storing and sharing scheme over decentralized consortium blockchain. Through an "on-chain & off-chain" model, it relieves the massive data storage pressure of medical blockchain. By introducing an improved proxy re-encryption mechanism, the proposed scheme realizes the fine-gained access control of the outsourced data, and can also prevent the collusion between semi-trusted cloud servers and data requestors who try to reveal EMRs without authorization. Compared with the existing schemes, our solution can provide a lower computational overhead in repeated EMRs sharing, resulting in a more efficient overall performance.

Eman Ashraf, Nihal F. F. Areed, Hanaa Salem, Ehab H. Abdelhay · 5 authors

Recently, there has been considerable growth in the internet of things (IoT)-based healthcare applications; however, they suffer from a lack of intrusion detection systems (IDS). Leveraging recent technologies, such as machine learning (ML), edge computing, and blockchain, can provide suitable and strong security solutions for preserving the privacy of medical data. In this paper, FIDChain IDS is proposed using lightweight artificial neural networks (ANN) in a federated learning (FL) way to ensure healthcare data privacy preservation with the advances of blockchain technology that provides a distributed ledger for aggregating the local weights and then broadcasting the updated global weights after averaging, which prevents poisoning attacks and provides full transparency and immutability over the distributed system with negligible overhead. Applying the detection model at the edge protects the cloud if an attack happens, as it blocks the data from its gateway with smaller detection time and lesser computing and processing capacity as FL deals with smaller sets of data. The ANN and eXtreme Gradient Boosting (XGBoost) models were evaluated using the BoT-IoT dataset. The results show that ANN models have higher accuracy and better performance with the heterogeneity of data in IoT devices, such as intensive care unit (ICU) in healthcare systems. Testing the FIDChain with different datasets (CSE-CIC-IDS2018, Bot Net IoT, and KDD Cup 99) reveals that the BoT-IoT dataset has the most stable and accurate results for testing IoT applications, such as those used in healthcare systems.

Yuan Liu, Chuang Zhang, Yu Yan, Xin Zhou · 6 authors

IoT data exchange plays a vital role in supporting various applications and services with massive IoT devices. However, the existence of malicious devices threatens the integrity and reliability of the exchanged data. Trust management has been used to mitigate the impact of malicious devices in centralized and decentralized architectures. However, most of these traditional trust management systems bear computation, storage, and communication challenges. In this study, we propose a semi-centralized trust management system architecture based on blockchain in both single and multiple domains. The IoT devices are centralized organized by cloud servers who coordinately sustain a rating data ledger within each domain based the proposed rotation based consensus protocol in a decentralized manner to support cross-domain data exchange. A computational trust model is proposed by aggregating the direct and indirect trust information, where we elaborately design decay function, recommendation credibility and adaptable weights so as to calculate the trust value of dynamic malicious devices. Finally, we evaluate the proposed system model in various situations through simulation based experiments and compare it with two classical models in the literature. The experimental results demonstrate the effectiveness of the proposed trust model in identifying malicious devices and mitigating the influence of malicious devices.

Pengxiang Zhao, Jesus Rodrigo Cedeno Jimenez, Maria Antonia Brovelli, Ali Mansourian

Abstract. In recent years, geospatial big data has been generated at a very high speed, and the data volume is becoming increasingly massive. In order to realize the full potential of geospatial big data, there has been a strong requirement and push to embrace the value of open science. However, it is still challenging to preserve the privacy and integrity of geospatial data in data sharing and management. Blockchain as d distributed ledger technology has a series of good characteristics, such as decentralization, trust-free, transparency, tamper-free, consensus and security, etc. These characteristics of blockchain are beneficial for facilitating geospatial data sharing and management, and hence promoting the development of open GIS. In this paper, we provide a comprehensive review on the literature that involves how blockchain technology is applied to geospatial data, especially in geospatial data privacy and integrity preservation. First, the background knowledge on geospatial data privacy and blockchain technology are introduced. Then, we reviewed how blockchain technology is applied to geospatial data, followed by the conclusion of the topic. This review is beneficial for understanding how blockchain technology can be applied to geospatial domain by integrating geospatial technologies like GIS and remote sensing.

Abdul Rehman Javed, Muhammad Abul Hassan, Faisal Shahzad, Waqas Ahmed · 7 authors

The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.

Yihuai Liang, Yan Li, Byeong‐Seok Shin

No abstract is available for this record.

Rafael Belchior, Limaris Torres, Jonas Pfannschmid, André Vasconcelos · 5 authors

Distributed ledger technology (DLT) provides decentralized and tamper-resistant data storage, replicated among mutually untrusting participants. With the advancement of this technology, different privacy-preserving blockchains have been proposed, such as Corda, Hyperledger Fabric, and Digital Asset’s Canton. These distributed ledgers only provide \emph{partial consistency}, which implies that participants can view the same ledger differently. A \emph{view} represents the states of a blockchain available to a particular stakeholder. The combination of views forms an integrated view that represents a consistent global state shared by all participants. This paper introduces BUNGEE (Blockchain UNifier view GEnErator), the first DLT view generator, to allow capturing DLT snapshots, constructing views, and performing arbitrary operations on those, such as integrating views. Creating and integrating views allows interesting applications, such as stakeholder-centric snapshots for audits, cross-chain analysis, blockchain migration, and data analytics.

Zewei Liu, Chunqiang Hu, Hui Xia, Tao Xiang · 6 authors

Currently, the conventional mode of power data transaction is mediated by Web pages. Nevertheless, there are challenging issues such as privacy protection, transaction security and data reliability in power data trading. In this paper, we present a novel secure power data trading scheme (SPDTS). Firstly, the zero-knowledge proof is employed to achieve data availability and consistency without revealing the data. Then, SPDTS takes full advantage of the dispersibility and immutability of blockchain to ensure the reliability of data transactions. To keep the transaction process efficient, the processing tasks for power data are performed under smart contract. Meanwhile, a trusted execution environment (TEE) is adopted to guarantee the security of power data. Finally, we present a differential privacy scheme to safeguard the privacy information in the power data. Our study indicates that the proposed scheme can achieve privacy protection, transaction security and data reliability. Also, we conduct security analysis and verify the privacy protection property of the scheme in real cases.