The privacy-preserving data aggregation is a critical problem for many applications where multiple parties need to collaborate with each other privately to arrive at certain results. Blockchain, as a database shared across the network, provides an underlying platform on which such aggregations can be carried out with a decentralized manner. Therefore, in this paper, we have proposed a scalable privacy-preserving data aggregation protocol for summation on the Ethereum blockchain by integrating several cryptographic primitives including commitment scheme, asymmetric encryption and zero-knowledge proof along with the hypercube network topology. The protocol consists of four stages as contract deployment, user registration, private submission and proof verification. The analysis of the protocol is made with respect to two main perspectives as security and scalability including computational, communicational and storage overheads. In the paper, the zero-knowledge proof, smart contract and web user interface models for the protocol are provided. We have performed an experimental study in order to identify the required gas costs per individual and per system. The general formulation is provided to characterize the changes in gas costs for the increasing number of users. The zero-knowledge proof generation and verification times are also measured.
E. Suresh Babu, Mekala Srinivasa Rao, Gandharba Swain, A. Kousar Nikhath · 5 authors
Abstract The technological integration of the Internet of Things (IoT)‐Cloud paradigm has enabled intelligent linkages of things, data, processes, and people for efficient decision making without human intervention. However, it poses various challenges for IoT networks that cannot handle large amounts of operation technology (OT) data due to physical storage shortages, excessive latency, higher transfer costs, a lack of context awareness, impractical resiliency, and so on. As a result, the fog network emerged as a new computing model for providing computing capacity closer to IoT edge devices. The IoT‐Fog‐Cloud network, on the other hand, is more vulnerable to multiple security flaws, such as missing key management problems, inappropriate access control, inadequate software update mechanism, insecure configuration files and default passwords, missing communication security, and secure key exchange algorithms over unsecured channels. Therefore, these networks cannot make good security decisions, which are significantly easier to hack than to defend the fog‐enabled IoT environment. This paper proposes the cooperative flow for securing edge devices in fog‐enabled IoT networks using a permissioned blockchain system (pBCS). The proposed fog‐enabled IoT network provides efficient security solutions for key management issues, communication security, and secure key exchange mechanism using a blockchain system. To secure the fog‐based IoT network, we proposed a mechanism for identification and authentication among fog, gateway, and edge nodes that should register with the blockchain network. The fog nodes maintain the blockchain system and hold a shared smart contract for validating edge devices. The participating fog nodes serve as validators and maintain a distributed ledger/blockchain to authenticate and validate the request of the edge nodes. The network services can only be accessed by nodes that have been authenticated against the blockchain system. We implemented the proposed pBCS network using the private Ethereum 2.0 that enables secure device‐to‐device communication and demonstrated performance metrics such as throughput, transaction delay, block creation response time, communication, and computation overhead using state‐of‐the‐art techniques. Finally, we conducted a security analysis of the communication network to protect the IoT edge devices from unauthorized malicious nodes without data loss.
Aitizaz Ali, Bander Ali Saleh Al‐rimy, Ting Tin Tin, Saad Altamimi · 6 authors
Precision medicine has emerged as a transformative approach to healthcare, aiming to deliver personalized treatments and therapies tailored to individual patients. However, the realization of precision medicine relies heavily on the availability of comprehensive and diverse medical data. In this context, blockchain-enabled federated learning, coupled with electronic medical records (EMRs), presents a groundbreaking solution to unlock revolutionary insights in precision medicine. This abstract explores the potential of blockchain technology to empower precision medicine by enabling secure and decentralized data sharing and analysis. By leveraging blockchain's immutability, transparency, and cryptographic protocols, federated learning can be conducted on distributed EMR datasets without compromising patient privacy. The integration of blockchain technology ensures data integrity, traceability, and consent management, thereby addressing critical concerns associated with data privacy and security. Through the federated learning paradigm, healthcare institutions and research organizations can collaboratively train machine learning models on locally stored EMR data, without the need for data centralization. The blockchain acts as a decentralized ledger, securely recording the training process and aggregating model updates while preserving data privacy at its source. This approach allows the discovery of patterns, correlations, and novel insights across a wide range of medical conditions and patient populations. By unlocking revolutionary insights through blockchain-enabled federated learning and EMRs, precision medicine can revolutionize healthcare delivery. This paradigm shift has the potential to improve diagnosis accuracy, optimize treatment plans, identify subpopulations for clinical trials, and expedite the development of novel therapies. Furthermore, the transparent and auditable nature of blockchain technology enhances trust among stakeholders, enabling greater collaboration, data sharing, and collective intelligence in the pursuit of advancing precision medicine. In conclusion, this abstract highlights the transformative potential of blockchain-enabled federated learning in empowering precision medicine. By unlocking revolutionary insights from diverse and distributed EMR datasets, this approach paves the way for a future where healthcare is personalized, efficient, and tailored to the unique needs of each patient.
With the increasing presence of blockchain-based distributed applications in various aspects of daily life, there has been a growing focus on the privacy protection of blockchain ledgers and the corresponding regulatory technologies. However, current mainstream solutions primarily concentrate on the verifiable encryption of blockchain transaction addresses and contents, neglecting the regulatory requirements for private transactions. Moreover, the few monitorable solutions suffer from issues such as excessive centralization and a single-minded approach to regulatory content. To address these deficiencies, this paper proposes a blockchain privacy-preserving scheme that supports multi-level regulation through the utilization of zero-knowledge proofs (zk-SNARKs) and attribute-based encryption (ABE). Firstly, by leveraging zk-SNARKs, this scheme achieves blockchain privacy-preserving within an account model, enabling the concealment of user transaction addresses and values. Secondly, by employing attribute-based encryption, a multi-level regulatory model is developed alongside the privacy protection measures, allowing for selective disclosure of transaction content. Finally, we analyze the security of the proposed scheme and compare it with other schemes, discussing its advantages in terms of privacy, security, and regulatory capabilities, we also provide a preliminary evaluation of the scheme's efficiency through experiments. In conclusion, the scheme demonstrates strong privacy by relying on mathematical proofs through zk-SNARKs to ensure security while comprehensively safeguarding content. It also achieves multi-level regulation on the foundation of privacy protection, with comprehensive regulatory coverage and decentralized regulatory authority.
Federated learning (FL) aims to collaboratively train a global model while ensuring client data privacy. However, FL faces challenges from the non-IID data distribution among clients. Clustered FL (CFL) has emerged as a promising solution, but most existing CFL frameworks adopt synchronous frameworks lacking asynchrony. An asynchronous CFL framework called SDAGFL based on directed acyclic graph distributed ledger techniques (DAG-DLT) was proposed, but its complete decentralization leads to high communication and storage costs. We propose DAG-ACFL, an asynchronous clustered FL framework based on directed acyclic graph distributed ledger techniques (DAG-DLT). We first detail the components of DAG-ACFL. A tip selection algorithm based on the cosine similarity of model parameters is then designed to aggregate models from clients with similar distributions. An adaptive tip selection algorithm leveraging change-point detection dynamically determines the number of selected tips. We evaluate the clustering and training performance of DAG-ACFL on multiple datasets and analyze its communication and storage costs. Experiments show the superiority of DAG-ACFL in asynchronous clustered FL. By combining DAG-DLT with clustered FL, DAG-ACFL realizes robust, decentralized and private model training with efficient performance.
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.
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
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.
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
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.
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.
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.
The reliable circulation of automotive supply chain data is crucial for automotive manufacturers and related enterprises as it promotes efficient supply chain operations and enhances their competitiveness and sustainability. However, with the increasing prominence of privacy protection and information security issues, traditional data sharing solutions are no longer able to meet the requirements for highly reliable secure storage and flexible access control. In response to this demand, we propose a secure data storage and access control scheme for the supply chain ecosystem based on the enterprise-level blockchain platform Hyperledger Fabric. The design incorporates a dual-layer attribute-based auditable access control model for access control, with four smart contracts aimed at coordinating and implementing access policies. The experimental results demonstrate that the proposed approach exhibits significant advantages under large-scale data and multi-attribute conditions. It enables fine-grained, dynamic access control under ciphertext and maintains high throughput and security in simulated real-world operational scenarios.
Artificial intelligence (AI) is a very powerful technology and can be a potential disrupter and essential enabler. As AI expands into almost every aspect of our lives, people raise serious concerns about AI misbehaving and misuse. To address this concern, international organizations have put forward ethics guidelines for constructing trustworthy AI (TAI), including privacy, transparency, fairness, robustness, accountability, and so on. However, because of the black-box characteristics and complex models of AI systems, it is challenging to translate these guiding principles and aspirations into AI systems. Blockchain, an important decentralized technology, can provide the capabilities of transparency, traceability, immutability, and secure sharing and hence can be used to make AI trustworthy. In this paper, we survey studies on blockchain-based TAI (BTAI) from a software development lifecycle view. We classify the lifecycle of BTAI into four stages: Planning, data collection, model development, and system deployment/use. Particularly, we investigate and summarize the trustworthy issues that blockchain can achieve in the latter three stages, including (1) data transparency, privacy, and accountability; (2) model transparency, privacy, robustness, and fairness; and (3) robustness, privacy, transparency, and fairness of system deployment/use. Finally, we present essential open research issues and future work on developing BTAI systems.
S Sultana, Ch. Rupa, R. Pavana Malleswari, Thippa Reddy Gadekallu
In the digital age, ensuring the authenticity and security of academic certificates is a critical challenge faced by educational institutions, employers, and individuals alike. Traditional methods for verifying academic credentials are often cumbersome, time-consuming, and susceptible to fraud. However, the emergence of blockchain technology offers a promising solution to address these issues. The proposed system utilizes a blockchain network, where each academic certificate is stored as a digital asset on the blockchain. These digital certificates are cryptographically secured, timestamped, and associated with unique identifiers, such as hashes or public keys, ensuring their integrity and immutability. Anyone with access to the blockchain network can verify a certificate’s authenticity, using the MetaMask extension and Ethereum network, eliminating the need for intermediaries and reducing the risk of fraudulent credentials. The main strength of the paper is that the data that are stored in the blockchain are unique identifiers of the encrypted data, which is encrypted by using an encryption technique that provides more security to the academic certificates. Furthermore, IPFS is also used to store large amounts of encrypted data.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Xiaohong Zhang, Jiaming Lai, Ata Jahangir Moshayedi
Abstract Vehicular ad hoc networks (VANETs) is the hotspot research field of wireless mobile ad hoc network, it provides a new opportunity to create a safe and efficient transportation environment. However, as an open network where information has to interact frequently, it is difficult to ensure the security of data transmitted in VANETs and protect the privacy of drivers. Many existing information-sharing schemes use complex encryption algorithms to enable secure traffic data sharing. Nevertheless, these schemes are not suitable for VANETs because of their high computational overhead and lack of corresponding tracking mechanisms for malicious vehicles. Therefore, a traffic data security sharing scheme is designed that combines blockchain technology and traceable ring signature algorithms to secure the transmitted messages. The traceable ring signature algorithm is formulated in combination with bilinear pairing, enabling conditional privacy protection instead of traditional ring signature. To improve the efficiency of VANETs, this scheme introduces edge computing technology to reduce the computational burden of Road Side Units (RSUs) by offloading most of the computational tasks to the servers via edge nodes. In addition, we use smart contract to track malicious vehicles. Security analysis and performance comparison show that our scheme is more efficient and secure for drivers than other existing related schemes.
With the development of the Internet and edge computing technology, many industrial Internet applications have emerged, followed by a large amount of data in the Industrial Internet. How to securely store and share this data has become a hot topic in current research. Firstly, a blockchain and IPFS based on-chain and off-chain storage architecture is proposed, which detects data through machine learning algorithms and stores data without anomalies. Then, the roles of users were classified and various smart contracts were designed for registering users, deleting users, and managing roles and their permissions. Finally, an experimental platform was built through open-source software to verify the feasibility of the proposed scheme and realize the safe storage and sharing of normal data.
Stealth addresses are a privacy-enhancing technology that provides recipient anonymity on blockchains. In this work, we investigate the recipient anonymity and unlinkability guarantees of Umbra, the most widely used implementation of the stealth address scheme on Ethereum, and its three off-chain scalability solutions, i.e., Arbitrum, Optimism, and Polygon. Specifically, we define and evaluate four heuristics to uncover the real recipients of stealth payments. We find that for the majority of Umbra payments, it is straightforward to establish the recipient, hence nullifying the benefits of using Umbra. In particular, we identify the real recipient of 48.5%, 25.8%, 65.7%, and 52.6% of all Umbra transactions on the Ethereum main net, Polygon, Arbitrum, and Optimism networks, respectively. Finally, we suggest easily implementable countermeasures to evade our deanonymization and linking attacks.