Syed Sajid Ullah, Vladimir Oleshchuk, Harsha S. Gardiyawasam Pussewalage
The Internet of Things (IoT) network is rapidly expanding due to sudden technological advancements, which enable objects to become intelligent and contribute to the network. Before permitting a newly added IoT device to communicate with the network, it is essential to provide access and authenticate the device’s legitimacy by ensuring that it has not been tampered. Recently, blockchain technology has been integrated into Attribute Based Access Control (ABAC) protocols to supply a more robust security mechanism for access control in IoT. The IoT and blockchain-based ABAC serve as the foundation of this survey, offering a comprehensive introduction to both topics. Additionally, several security concerns and vulnerabilities associated with Blockchain Envisioned ABAC (BE-ABAC) are presented. A Comparison with related surveys has also been made. Besides, we also present a comparative analysis based on evaluation based on Distance from Average Solution (EDAS) to rank the best schemes among the suggested BE-ABAC schemes. In conclusion, we discuss some open research challenges in an IoT network that uses blockchain to manage access control.
Tasiu Muazu, Yingchi Mao, Abdullahi Uwaisu Muhammad, Muhammad Ibrahim · 6 authors
As data sharing on the Internet of Medical Things (IoMT) become more complicated, the problems of divergent interests, unregulated policies, privacy and security, and the resource constraints of data owners have drawn the attention of researchers. To address the problems, this paper provides resource management in the IoMT using a proposed edge-empowered blockchain federated learning system. Also, an improved linear regressor model is proposed as the global learning model for the federated learning system. Gradient parameters are encrypted using Paillier encryption on the federated server side before they are shared by the federated clients. Blockchain is deployed to provide new security features for IoMT and edge computing. Moreover, all transactions of IoMT and edge devices are stored on the blockchain for secure cataloguing and auditing. Edge computing is employed to handle complex computing tasks on behalf of IoMT devices. Extensive simulations are conducted to validate the efficacy of the proposed system model. The results show that computing costs are minimized while still achieving the benefits of security and privacy in the proposed system. Furthermore, security analysis shows that the proposed system is protected from security attacks.
Data security and privacy protection are critical challenges that constrain the advancement of edge computing. Similarly, blockchain technology faces constraints in addressing security issues linked with edge computing due to its scalability limitations. To tackle these challenges and promote the development of blockchain technology, this paper presents a scheme that enhances privacy data protection in blockchain smart contracts using edge computing and a master-slave multichain architecture. Firstly, we propose a master-slave multichain architecture based on the traditional single chain and integrate it with a three-layer edge computing structure to address security issues on the edge side. We also design a signature authentication scheme utilizing ECC integrated with blockchain encryption technology. Secondly, we incorporate the role-based access control (RBAC) model with smart contracts to finely divide user privileges, construct an interdomain role-based access control (ID-RBAC) model, and provide detailed access authentication process designs for both within and between domains. Finally, experimental results demonstrate that our proposed scheme can effectively resist various attacks, significantly improve algorithm efficiency, and maintain a system overhead of less than 160 p, with a maximum transaction throughput of nearly 310 tx/s.
Zijian Bao, Debiao He, Huaqun Wang, Min Luo · 5 authors
With the proliferation of vehicular networking and data in the era of Vehicular Ad-hoc Networks (VANETs), ensuring privacy-preserving authentication and data privacy during analysis has emerged as a pivotal research focus. In this paper, we propose a blockchain-assisted privacy-preserving authentication protocolBapwith user-controlled data unlinkability for VANETs. We leverage Pointcheval-Sanders (PS) signatures to design a privacy-preserving authentication protocol that supports user traceability and revocation of malicious users. Additionally, we introduce an auxiliary data processor (DP) in our model to analyze VANET data with explicit and implicit linkability. The DP is capable of linking messages on the same or different topics, enabling minimal privacy disclosure during Big Data analysis. We provide a detailed proof and demonstrate that our scheme satisfies the required properties. Finally, performance evaluations demonstrate the efficiency of our proposed scheme. While supporting the mentioned functionalities, the gas cost is limited to a small range, and the signature size is only 354 bytes.
Large-Scale Heterogeneous Networks (LS-HetNets) integrate different types of networks enabled by various technologies into a global coverage network to offer seamless networking. Securing End-to-End (E2E) communication in LS-HetNets is an effective way to gain high communication security and user trust across multiple network domains. However, most of existing research on E2E security suffers from several severe drawbacks, such as single point of failures, identity privacy leakage, and poor generality. The literature still lacks a universal E2E communication security framework for LS-HetNets. In this paper, we propose Sec-E2E, a decentralized framework that uses blockchain to provide E2E communication security in LS-HetNets. It comprises several components that work in harmony to ensure secure and efficient E2E communications. Sec-E2E includes a blockchain-enabled protocol for User Equipment (UE) registration and key management, a privacy-preserving mutual authentication protocol that allows Serving Networks (SNs) and UEs to authenticate with each other quickly, and a secure session key generation and distribution scheme empowered with a Trusted Execution Environment (TEE). Security and performance evaluation demonstrates that Sec-E2E not only meets desired design goals of security and privacy, but also performs with higher efficiency compared with existing representative works.
Abstract Internet of Things (IoT) has rapidly expanded with the interconnection of various devices through wireless networks. However, this widespread deployment of IoT devices has posed challenges in managing access to device resources due to their vast quantity and scale. As these IoT devices generate and share sensitive data, ensuring secure access becomes paramount. Traditional access control systems like Discretionary Access Control (DAC), Intelligent Dynamic Bandwidth Access Control (IBAC), and Mandatory Access Control (MAC) have limitations such as centralization, single-point failure, and low throughput, making them less suitable for dynamic and diverse IoT environment. To address these challenges, Role-Based Access Control (RBAC) emerges as a promising solution as it provides fine-grained and dynamic access control based on user attributes, resource attributes, action attributes, environmental attributes, permissions, and environmental context. In parallel, Blockchain Technology (BCT) has gained attention as a distributed data management system, ensuring data integrity through decentralization, immutability, and consensus mechanisms. Kafka-based Hyperledger Fabric, an open-source blockchain platform, offers more efficient consensus mechanisms, higher throughputs, and support for multiple organizations and ledgers. This study proposes to develop a novel "KafkaIoT" system, which integrates Kafka-based Hyperledger Fabric with RBAC to create a distributed access control system for IoT. By leveraging blockchain's distributed architecture, KafkaIoT addresses the challenges of record tracking, dynamic access control management, and access control in the IoT landscape. The seamless integration of BCT with IoT can enhance security and enable the development of distributed IoT-based cryptocurrency services, contributing to a safer and more reliable IoT ecosystem.
Xueping Liang, Juan Zhao, Yan Chen, Eranga Bandara · 5 authors
BACKGROUND: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site. OBJECTIVE: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs. METHODS: We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain. RESULTS: We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra-based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. CONCLUSIONS: Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain.
Open access
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
This paper explores methods of assessing risk on-chain by efficiently integrating off-chain identity- and attribute-verification and on-chain transaction activity. While decentralized finance (DeFi) is a well-suited application of permissionless blockchains, it is severely constrained in its ability to reconcile real-world identities and, as a result, to quantify their associated transaction risk using solely on-chain data, limiting its realistic applications. Opening the ecosystem to a broader range of use cases requires consistent pseudonymity and quantifiable reputation. Traditional finance quantifies risk by collecting and vetting reputation information for an individual, such as credit scores or payment history. We explore a mechanism to incorporate these traditional trust factors into the DeFi ecosystem while both preserving individual privacy within a competitive and fair environment and retaining compatibility with existing platforms such as Ethereum. Even though blockchains are inherently public, our solution gives users control over the release of information that pertains to them. Consequently, our contribution focuses on customized methods that balance the level of disclosure of verfiable, provably-sourced user information against the likelihood of the user successfully gaining access to a desired resource, such as a loan under mutually-agreeable terms. Our solution is consistent with the zero-trust model in that it imports explicit trust from recognized sources through relevant, continuously updated metrics.
With the rapid development of the Industrial Internet, higher challenges are posed to the data security and privacy protection of traditional industrial Internet data sharing. At the present stage, the industrial Internet field has problems such as the lack of flexibility in access control, the difficulty in ensuring data availability, and the inability of data from different enterprises to circulate efficiently and securely, which hinders the development of the industrial Internet. In response to these challenges, a decentralized data-sharing solution based on CP-ABE is proposed to achieve fine-grained access control of industrial Internet data. To address issues related to centralized data storage and single points of failure, this solution combines distributed storage systems to enable distributed storage under the original data ciphertext and sharing of key ciphertext information chains, suitable for a decentralized industrial Internet data sharing architecture. To achieve the "availability and invisibility" of industrial Internet data, smart contracts and zero-knowledge proof technology are utilized to separate data ownership and data usage rights. Finally, security analysis and simulation experiments are conducted to evaluate the proposed scheme. The results demonstrate that the proposed solution effectively achieves fine-grained data access control while ensuring data security, and integrity, and meeting the increasing demands for industrial Internet data sharing efficiently.
Edgar Wallace Bowlin, Mohammad S. Khan, Biju Bajracharya, Bhargav Appasani · 5 authors
Current research with Vehicular Ad-hoc Networks (VANETs) has focused on adapting an efficient consensus mechanism and reducing the blockchain size while maintaining security. Care must be taken when implementing blockchains within VANET applications to leverage the chains’ strengths while mitigating their weaknesses. These chains can serve as distributed ledgers that provide storage for more than financial transactions. The security provided by longer blockchains constitutes a nearly immutable, decentralized data structure that can store any data relevant to the applications. However, these chains must be adapted to the ad-hoc, resource-constrained environments found in VANETs. In the absence of abundant resources and reliable network connections, chain operation and maintenance must address the challenges presented by highly mobile nodes in novel ways, including situations such as emergency messaging that require real-time responses. Researchers have included different mechanisms to realize lightweight blockchains, such as adding reputation to existing consensus mechanisms, condensing the consensus committees, using geographical information, and monitoring a nodes behavior in attempts to adapt blockchains to these domains. This paper analyzes the challenges and gives solutions for these different mechanisms to realize lightweight blockchains for VANETs.
In the era of data-centric driving enterprise business, how to effectively control data access and securely share data between enterprises has become a common concern. While blockchain's distributed ledger solves this problem in some ways, blockchain's asymmetric encryption is only for one-to-one secure transmissions. Therefore, this paper uses attribute-based encryption to propose a data sharing and access control model for enterprise data. Designed to achieve more precise access control and secure sharing. After comparison, the model can better solve the problem of data access control within enterprises and data sharing between enterprises.
The use of blockchain smart contracts in the monetization of IoT data assets addresses challenges in personal data ownership, quantifiable tracking of data assets, and efficient value transfer within IoT systems. By leveraging blockchain digital fingerprints, the ownership and control of data are transferred from device manufacturers to users, enabling individual data rights. Through techniques such as lifecycle management and digital signatures, device status and data hash values are stored on the blockchain, ensuring data reliability. Smart contracts are used to construct third-party data trading platforms, guaranteeing data sharing security and facilitating seamless data monetization and value transfer. Quantitative analysis of attack possibilities and success probabilities demonstrates that blockchain smart contract technology provides data tamper resistance and eliminates trust issues in data transactions. With the aid of blockchain smart contracts, the association of IoT data becomes feasible, facilitating the transfer and sharing of data values among IoT devices.
The prosperity of blockchain has pushed various decentralized applications, e.g., cross-regional finance, due to its advantages of openness, immutability, and decentralization. The feature of openness inevitably leads to a serious privacy breach. Recently, various privacy-enhanced works (e.g., Zcash, Monero) were proposed focusing on this problem. However, most existing solutions either aim for the unspent transaction output (UTXO) model, or fail to provide full privacy protection for the account-based model with efficient performance. In this paper, we put forwardLedgerMaze, an efficient privacy-preserving non-interactive zero-knowledge (NIZK) scheme over account-model blockchain. We design a novel scheme calledchequemechanism to cut the link between the sender/receiver relationship. Namely, a sender transfers money to a receiver's cheque, then the receiver can retrieve the cheque among a set of cheques for obfuscation without revealing the original one. We construct several efficient NIZK proofs for initializing the mechanism. Moreover, we further analyze the security properties ofLedgerMaze. Experimental results show thatLedgerMazeachieves comparable performance in communication and computation costs while retaining a full privacy guarantee, compared to previous similar constructions.
With the development of deep learning, the demand for similarity matching between texts in text classification is becoming increasingly high. How to match texts quickly under the premise of keeping private information secure has become a research hotspot. However, most existing protocols currently have full set limitations, and the applicability of these methods is limited when the data size is large and scattered. Therefore, this paper applies the secure vector calculation method for text similarity matching in the case of data without any complete set constraints, and it designs a secure computation protocol of text similarity (SCTS) based on the semi-honest model. At the same time, elliptic-curve cryptography technology is used to greatly improve the execution efficiency of the protocol. In addition, we also analyzed the possibility of the malicious behavior of participants in the semi-honest-model protocol, and further designed an SCTS protocol suitable for the malicious model using the cut-and-choose and zero-knowledge-proof methods. By proposing a security mechanism, this protocol aims to provide a reliable and secure computing solution that can effectively prevent malicious attacks and interference. Finally, through the analysis of the efficiencies of the existing protocols, the efficiencies of the protocols under the malicious model are further verified, and the practical value for text classification in deep learning is demonstrated.
Open access
Cryptography and Data Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
The use of blockchain in various fields is increasing rapidly because of its transparency and decentralisation. However, the transparency and immutability of data on the blockchain have prompted questions about data privacy. The intention of this study is to investigate the many theories and solutions made to deal with data privacy in blockchain applications. This paper covers an overview of privacy-preserving techniques like secure multi-party computation, ring signatures, homomorphic encryption, and zero-knowledge proofs, as well as the concept of local differential privacy in data privacy on blockchain. This paper also covers the data privacy challenges in blockchain applications. Overall, the aim of this article is to furnish the latest research and future trends regarding data privacy in blockchain applications and its importance.
Abstract A Blockchain is one distributed ledger system, and keeps the high redundancy of ledger copies to make the assurance of network security. However, the continuously duplicated full copies also impose a tremendous amount of demand on some nodes for data storage. The development of blockchain technologies in the IoT(Internet of Things) application scenario is hampered by the restricted storage capacities of terminal devices used in the IoT edge computing scenario, which makes it difficult to load the full copy with infinite growth. Our paper suggests a collaborative ledger storing model based on Chord Ring to address the issues with lightweight blockchains in data storage. On-chain blocks are split by Chord Ring structure and stored in various node clusters in a decentralized manner, and off-chain blocks at various levels are provided with PoW(Proof of Work) consensus asynchronously and archived centrally to the cloud storage on a regular basis. The theoretical and experimental analysis indicates that this model can reduce the data storage redundancy of blockchains while ensuring the high availability of data and the high decentralization of the network.
The Internet of Healthcare Things (IoHT) and blockchain technologies have made it feasible to share data in a secure and effective manner, but it is still challenging to ensure the data's veracity and privacy. This paper presents a blockchain authentication method that utilizes Machine Learning (ML) techniques that use smart contracts to ensure the security and privacy of IoHT data. The process utilizes smart contracts to manage access control and ensure data integrity, and deep learning algorithms to identify and validate the accuracy of user data. Furthermore, the approach improves the resilience and dependability of the authentication process and permits secure data ex-change between multiple IoHT systems. The proposed approach provides a potentially revolutionary solution to enhance the safety and confidentiality of IoHT data. It has the potential to fundamentally change how healthcare is provided in the future.
Public Key Infrastructure (PKI) is a system that provides a secure method of exchanging digital information by authenticating the identity of the parties involved and ensuring the confidentiality, integrity, and non-repudiation of the data being exchanged. However, in recent years, attacks on CAs in PKI have been rampant, such as CA intrusion and the issuance of fraudulent certificates, causing concerns during communication. As an emerging solution, distributed ledger technology provides data traceability and immutability, solving the issue of the original PKI lacking transparency and the difficulty in immediately detecting the issuance of fraudulent certificates. In this paper, we propose a distributed ledger technology-based certificate management mechanism for issuing, revoking, and updating certificates, and also providing a secure method of communication by using certificate authentication to verify identity and ensuring communication security.
In recent years, machine learning models have evolved, and the training of these models requires large amounts of data. However, the training data often contains sensitive information, raising privacy concerns. Federated Learning has been proposed as a solution to mitigate privacy risks. Despite its advantages, Federated Learning still faces challenges such as the aggregator being a single point of failure, the existence of malicious participants, and the lack of incentives. Combining Federated Learning with blockchain technology could potentially address these challenges. In this study, we propose a new method for asynchronous Federated Learning using blockchain smart contracts. Our proposed method operates autonomously and in a decentralized manner without the need to trust any central organization, making it trustless. We propose an algorithm that motivates workers to submit high-quality models as quickly as possible. Workers’ behaviors are driven by incentive mechanisms. We deployed a smart contract on a local Ethereum blockchain and executed multiple workers. Our evaluation results demonstrate that learning converges and achieves accuracy comparable to conventional Federated Learning, indicating the effectiveness of our proposed method.
Abel Yeboah-Ofori, Sayed Kashif Sadat, Iman Darvishi
Blockchain security issues in relation to encryption for data privacy and integrity in cloud computing have become challenging due to the decentralized and peer-to-peer systems for securing data storage and transfer in smart contracts. Further, Blockchain technology continues revolutionizing how we handle data, from improving transparency to enhancing security. However, various instances of data breaches, piracy, and hacking attacks have compromised the safety measures employed by these providers. The paper aims to explore Blockchain technology and how encryption algorithms are used to leverage security properties to uphold data privacy and integrity in a cloud environment to enhance security. The novelty contribution of the paper is threefold. First, we explore existing blockchain attacks, vulnerabilities, and their impact on the cloud computing environment supported by numerous cloud services that enable clients to store and share data online. Secondly, we used an encryption approach to detect data security by combining AES encryption, cloud storage, and Ethereum smart contracts in cloud AWS S3. Finally, we recommend control mechanisms to improve blockchain security in the cloud environment. The paper results show that AES algorithms can be used in blockchain smart contracts to enhance security, privacy, and integrity to ensure secure data in transit and at rest.
Abstract The rapid development of the Internet of Vehicles (IoV) along with the emergence of intelligent applications have put forward higher requirements for massive task offloading. Even though Mobile Edge Computing (MEC) can diminish network transmission delay and ease network congestion, the constrained heterogeneous resources of a single edge server and the highly dynamic topology of vehicular edge networks may compromise the efficiency of task offloading, including latency and energy consumption. Vehicular edge networks are also vulnerable to malicious outside attacks. In this paper, we propose a new blockchain-enabled digital twin vehicular edge network (DTVEN) where digital twin (DT) is exploited to monitor network communication, computation, and caching (3C) resources management in real time to provide rich data for offloading decision-making, and blockchain is utilized to secure fair and decentralized offloading transactions among DTs. To ensure 3C resources sharing across edge servers, we design a DT-assisted edge cooperation scheme, which makes full use of edge resources in vehicular networks. Furthermore, a DT-based smart contract is built to achieve a quick and effective consensus process. Then, we apply a task offloading algorithm based on an improved cuckoo algorithm (ICA) and a resource allocation scheme based on greedy strategy to minimize network cost by comprehensively taking into account latency and energy consumption. Numerical results demonstrate that our proposed scheme outperforms the existing schemes in terms of network cost.