The widespread use of UAVs in smart cities for tasks like traffic monitoring and environmental data collection creates significant privacy and security concerns due to the transmission of sensitive data. Traditional UAV-MEC systems with centralized data processing expose this data to risks like breaches and manipulation, potentially hindering the adoption of these valuable technologies. To address this critical challenge, we propose UBFL, a novel privacy-preserving federated learning mechanism that integrates blockchain technology for secure and efficient data sharing. Unlike traditional methods relying on differential privacy (DP), UBFL employs an adaptive nonlinear encryption function to safeguard the privacy of UAV model updates while maintaining data integrity and accuracy. This innovative approach enables rapid convergence, allowing the base station to efficiently identify and filter out severely compromised UAVs attempting to inject malicious data. Additionally, UBFL incorporates the Random Cut Forest (RCF) anomaly detection algorithm to actively identify and mitigate poisoning data attacks. Extensive comparative experiments on benchmark datasets CIFAR10 and Mnist demonstrably showcase UBFL's effectiveness. Compared to DP-based methods, UBFL achieves accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%) in privacy preservation while maintaining superior accuracy. Notably, under data pollution scenarios with varying attack sample rates (10%, 20%, and 30%), UBFL exhibits exceptional resilience, highlighting its robust capabilities in securing UAV gradients within MEC environments.
Federated Learning is a revolutionary approach to machine learning. Its purpose is to enable multiple participants to collaboratively train machine learning models without the need to share local data. The main objective is to address issues related to data privacy and security. In traditional machine learning, data is typically centralized and stored in a single location or on cloud servers for training. However, this centralized training approach carries risks of potential data leakage, especially concerning sensitive and critical information. Industries such as healthcare and finance, which involve sensitive data, place a premium on safeguarding data privacy. Furthermore, in cases where data cannot be easily transferred or is subject to privacy regulations, centralized methods may face limitations. Federated Learning revolutionizes the conventional approach by introducing a decentralized model training process. It maintains data decentralization while achieving collaborative model optimization, greatly enhancing data privacy and security. The MOON algorithm, an integral part of federated learning, contributes to its novelty. As a significant component, the MOON algorithm facilitates new possibilities for federated learning. In this article, the research will elaborate on the MOON algorithm within the context of federated learning. And this article will delve into its description and optimization, elucidating how it enhances federated learning.
With the vigorous development of the social network, massive data have generated by large amount of users. To utilize these data, social computing is widely used. However, due to the conflict between proprietary data and the use of data, it is a challenge to fully obtain data value in an efficient and legal way. Moreover, how to manage, govern and schedule data, a technical and feasible approach is still a major problem. To this end, as an emerging distributed ledger technology, blockchain is considered a promising technology for data security and privacy and is introduced for cross-domain data governance. In this paper, we propose a blockchain-assisted cross-domain data sharing scheme for social computing data governance. Specifically, permissioned blockchain is introduced to construct trust among different parties. A zero-knowledge proof scheme is designed to verify data ownership confidentially and anonymously. The security of the zero-knowledge proof scheme is also analyzed. The extensive simulations and experiments have proven the effectiveness and efficiency of the mechanism.
In the era of data-driven healthcare, the amalgamation of blockchain and Federated Learning (FL) introduces a paradigm shift towards secure, collaborative, and patient-centric data-sharing. This paper pioneers the exploration of the conceptual framework and technical synergy of FL and blockchain for decentralized data-sharing, aiming to strike a balance between data utility and privacy. FL, a decentralized machine learning paradigm, enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data. Combined with blockchain, a transparent and immutable ledger, it establishes an ecosystem fostering trust, security, and data integrity. The paper elucidates the technical foundations of FL and blockchain, unravelling their roles in reshaping healthcare data-sharing. The paper vividly illustrates the potential impact of this fusion on patient care. The proposed approach preserves patient privacy while granting healthcare providers and researchers access to diversified datasets, ultimately leading to more accurate models and improved diagnoses. The findings underscore the potential acceleration of medical research, improved treatment outcomes, and patient empowerment through data ownership. The synergy of FL and blockchain envisions a healthcare ecosystem that prioritizes individual privacy and propels advancements in medical science.
Blockchain is a distributed ledger technology that possesses characteristics such as decentralization, tamper resistance, and programmability. However, while blockchain ensures transaction openness and transparency, transaction privacy is also at risk of being exposed. Therefore, this paper proposes the blockchain transaction privacy protection method based on the stealth address and the note mechanism to address the privacy leakage risk in blockchain public environments. Firstly, the proposed method generates a random seed known only to the parties involved based on the Diffie–Hellman key exchange protocol, ensuring the privacy of transactions. Then, it utilizes the Note Commitments table to maintain the binding relationship between the stealth address and the corresponding note, enabling efficient transfer and verification of note ownership. The uniqueness of the stealth address is utilized as an invalidation identifier for notes in the Nullifier table, ensuring efficient verification of the correctness of note invalidation identifiers. Additionally, this method employs Pedersen commitment and Bulletproofs range proof to generate proof of the legality of transaction amounts, enabling the concealment of transaction amounts and facilitating private transactions between the parties involved. Finally, this paper presents a detailed performance analysis, implementation, and testing of the method. From the results, it can be concluded that the method proposed can effectively prevent fraudulent behavior by various transaction participants and ensure the security, privacy, and integrity of the transaction. Critical processes consume only milliseconds, and the related commitments and proofs are also minimal, which is crucial for controlling transaction costs. At the same time, this method achieves a completely decentralized privacy transaction solution.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Our cyber security learning system involves an interactive environment for students to practice executing different attack and defense techniques relating to cyber security concepts. We intend to use a blockchain database to secure data from this learning system. The data being secured are students' scores accumulated by successful attacks or defends from the other students' implementations. As more professionals are departing from traditional relational databases, the enthusiasm around distributed ledger databases is growing, specifically blockchain. With many available platforms applying blockchain structures, it is important to understand how this emerging technology is being used, with the goal of utilizing this technology for our learning system. In order to successfully secure the data and ensure it is tamper resistant, an investigation of blockchain technology use cases must be conducted. In addition, this paper defined the primary characteristics of the emerging distributed ledgers or blockchain technology, to ensure we effectively harness this technology to secure our data. Moreover, we explored using a blockchain database for our data.
Shiuh‐Pyng Shieh, J. Voas, Phil Laplante, Jason Rupe · 9 authors
The convergence of technologies is happening across various aspects, such as communication, computing, medicine, and transportation. The smartphone is a perfect example of convergence, packing features, such as a camera, GPS, artificial intelligence, and Internet connectivity into one sleek device. Autonomous driving is another good example. In a time of rapidly converging technologies, reliability engineering must take into account the potential for cyber threats, the need for cyber trust, the importance of cyber security, and the criticality of cyber resilience. In this way, reliability engineers can ensure the confidentiality, integrity, and availability of computer systems and networks in the face of evolving threats and changing technologies. In this article, we introduce the challenges and current progress of reliability engineering in emerging technologies, including practices and applications of cyber trust and security, AI-empowered autonomous driving systems, modern mobile networks, blockchains and distributed ledger technologies, prognostic and health management, integrated circuit and hardware, and enterprise cybersecurity and threat hunting.
This paper introduces the zk-IoT framework, a novel approach to enhancing the security of Internet of Things (IoT) ecosystems through the use of Zero-Knowledge Proofs (ZKPs) on blockchain platforms. Our framework ensures the integrity of firmware execution and data processing in potentially compromised IoT devices. By leveraging the concept of ZKP, we establish a trust layer that facilitates secure, autonomous communication between IoT devices in environments where devices may not inherently trust each other. The framework includes zk-Devices, which utilize functional commitment to generate proofs for executed programs, and service contracts for encoding interaction logic among devices. It also utilizes a blockchain layer and a relayer as a ZKP storage and data communication protocol, respectively. Our experiments demonstrate that proof generation, reading, and verification take approximately 694, 5078, and 19 milliseconds in our system setup, respectively. These timings meet the practical requirements for IoT device communication, demonstrating the feasibility and efficiency of our solution. The zk-IoT framework represents a significant advancement in the realm of IoT security, paving the way for reliable and scalable IoT networks across various applications, such as smart city infrastructures, healthcare systems, and industrial automation.
Shrabani Sutradhar, S. P. Majumder, Rajesh Bose, Haraprasad Mondal · 5 authors
The Internet of Medical Things (IoMT) has transformed healthcare, collecting and transmitting vast medical data. This study proposes an innovative solution, integrating blockchain into IoMT within a fog-cloud computing framework for secure medical data transmission. The blockchain-based zero-trust system ensures reliable data auditing and Electronic Health Repository (EHR) protection. New blockchain entries atop prior blocks deter tampering, with Quad Merkle tree and zero-knowledge proof encryption ensuring data integrity and privacy. The multi-critic deep deterministic policy gradient algorithm optimizes task-offloading decisions in the fog-cloud layer. Security analyses validate its effectiveness, improving computing efficiency and ensuring data fidelity and privacy. These findings position the system as a promising solution for enhancing medical data security and integrity in edge-fog cloud environments, aligning with evolving healthcare technology demands.
To ensure the aggregation of a high-quality global model during the data-sharing process in the Internet of Vehicles (IoV), current approaches primarily utilize gradient detection to mitigate malicious or low-quality parameter updates. However, deploying gradient detection in plain text neglects adequate privacy protection for vehicular data. This paper proposes the IoV-BDSS, a novel data-sharing scheme that integrates blockchain and hybrid privacy technologies to protect private data in gradient detection. This paper utilizes Euclidean distance to filter the similarity between vehicles and gradients, followed by encrypting the filtered gradients using secret sharing. Moreover, this paper evaluates the contribution and credibility of participating nodes, further ensuring the secure storage of high-quality models on the blockchain. Experimental results demonstrate that our approach achieves data sharing while preserving privacy and accuracy. It also exhibits resilience against 30% poisoning attacks, with a test error rate remaining below 0.16. Furthermore, our scheme incurs a lower computational overhead and faster inference speed, markedly reducing experimental costs by approximately 26% compared to similar methods, rendering it suitable for highly dynamic IoV systems with unstable communication.
Abstract Smart devices are widely used in every application area and they serve specific application purposes. While some devices have their own large memory and processing unit, others are limited to data collection without processing capabilities. The land registry, one of the oldest processes for economic growth and governance, traditionally involves mapping the land in the field and collecting data on-site. Despite the shift to online processes, measurement and verification still rely on the traditional system. Blockchain technology, known for its transparency, immutability, speed, security, and decentralized storage, processes data on an immutable distributed ledger. Each node in the network maintains an updated copy of the ledger after each block addition. In the context of land registry, blockchain records every detail while addressing security, privacy, and smart network challenges. In the proposed system, a geolocation-based land registry using blockchain technology is utilized to enhance security, transparency, privacy, and speed compared to existing blockchain-based land registry systems. The proposed scheme follows a three-step registry process. The first step involves handling previous owner records, while the second step focuses on creating new records using smart devices and blockchain. The third step includes record verification and finalization of the registry process with the new owner. This process ensures personal data minimization, secure transactions, and payment of registry fees using blockchain technology. Smart devices play a crucial role in the proposed scheme by verifying the correct land, taking land measurements, and directly recording entries on the blockchain. Based on geolocations and areas, all required parameters for the registry are calculated. The implementation of the proposed system can prevent fraud and document forgeries in the land registry. A security analysis is conducted, and the system is implemented using multichain blockchain. Smart filters facilitate communication between sellers and buyers. Overall, the proposed system offers a hassle-free, geolocation-based, privacy-preserving land registry using blockchain technology.
Tácito Augusto Farias Júnior, Rafael Oliveira Vasconcelos, Admilson de Ribamar Lima Ribeiro
Privacy protection ensures that individuals have control over personal data, preventing abuse and preserving trust in the use of online services. In the “Digital Era”, where the collection, storage and processing of personal information have become ubiquitous, data privacy emerges as a relevant topic. In this sense, laws were created, such as the General Data Protection Law (LGPD) in Brazil and the General Data Protection Regulation (GDPR) in Europe, to control privacy and the processing of personal data. The article presents a comparative analysis of 2 (two) data privacy mechanisms, the Zero-Knowledge Proof (ZKP) and Ring Signatures, used in Blockchain, aiming at the legal and regulatory implications with the LGPD and GDPR. The comparative study between ZKP and Ring Signatures highlights the flexibility of ZKP in various contexts, including voting and secure authentication systems, while Ring Signatures offer significant advantages in terms of scalability and efficiency in systems where subscriber anonymity is considered fundamental. Furthermore, the legal and regulatory implications of the ZKP are discussed, mainly in relation to LGPD and GDPR. Finally, the article concludes that the comparative analysis offers insights into applications, challenges and legal and regulatory implications, particularly in relation to data privacy and compliance with regulations such as LGPD and GDPR.
Federated leaning (FL) has been frequently used in various field of studies and businesses. Traditional centralized FL systems suffer from serious issues. To address these concerns, decentralized federated learning (DFL) systems have been introduced in recent years in which with the help of blockchains, try to achieve more integrity and efficiency. On the other hand, privacy-preserving is an uncovered part of these systems. To address this, and also scaling the blockchain-based computations, we propose a zero knowledge proof (ZKP) based aggregator (zkDFL) that allows clients to share their large-scale model parameters with a trusted centralized server without revealing their individual data to other clients. We utilize blockchain technology to manage the aggregation algorithm via smart contracts. The server performs a ZKP algorithm to prove to the clients that the aggregation is done according to the accepted algorithm. The server can also prove that all inputs of clients have been used. We evaluate our measure through a public dataset about wearable internet of things. As demonstrated by numerical evaluations, zkDFL introduces verifiability of correctness of aggregation process and enhances the privacy protection and scalability of DFL systems, while the gas cost has declined significantly.
This paper explores the relationship between the development of the internet and health care, highlighting their parallel growth and mutual influence. It delves into the transition from the early, static days of Web 1.0, akin to siloed physician expertise in health care, to the more interactive and patient-centric era of Web 2.0, which was accompanied by advancements in medical technologies and patient engagement. This paper then focuses on the emerging era of Web3-the decentralized web-which promises a transformative shift in health care, particularly in how patient data are managed, accessed, and used. This shift toward Web3 involves using blockchain technology for decentralized data storage to enhance patient data access, control, privacy, and value. This paper also examines current applications and pilot projects demonstrating Web3's practical use in health care and discusses key questions and considerations for its successful implementation.
Presently, resource-constrained devices in manufacturing Internet of Things (MIoT), such as sensors and radio frequency identification (RFID) devices, collect a large amount of privacy-sensitive data. However, weak passwords and vulnerable encryption capabilities in MIoT have often become loopholes of security risks. To this end, this paper proposes a novel secure data-sharing scheme based on the integration of blockchain and fusion of both real and fake data to address the data security requirements of resource-constrained MIoT devices. First, a computing resource collaboration architecture is designed, thereby enabling these devices to interface with multiple devices with full resource to implement flexible resource scheduling. Then, a resource-assistance mechanism is devised through blockchain-based smart contracts by utilizing the idle resources of full nodes to complement the computational tasks launched by resource-constrained nodes. In addition, polygon semantic rules are proposed to improve the security of private data. Subsequently, real data artifacts are generated by data tampering to achieve privacy cover, avoiding the consumption of computing resources in traditional encryption algorithms. Finally, the feasibility of the proposed scheme is verified on a customized candy production line. The experimental results validate that data protection using polygon semantic rules can prevent actual data from being peeped. Moreover, the results also indicate that the proposed method can obtain resource assistance from other nodes through the resource compensation mechanism.
Abstract Internet of Medical Things (IoMT) has typical advancements in the healthcare sector with rapid potential proof for decentralised communication systems that have been applied for collecting and monitoring COVID‐19 patient data. Machine Learning algorithms typically use the risk score of each patient based on risk factors, which could help healthcare providers decide about post‐COVID‐19 care and follow‐up where the data privacy is another severe concern. The authors investigate the applicability of a distributed reinforcement learning approach in a Federated Learning (FL) multi‐disciplinary reinforcement system and explores the potential benefits of incorporating Blockchain Technology (BT) in the distributed system. Intermediate dependency features and transactions are avoided by applying Blockchain‐enabled reinforcement FL for the post‐COVID‐19 patient data of IoMT applications. The proposed approach helps to improvise clinical monitoring and ensure secure communication and data privacy in a decentralised manner. The main objective is to improve the efficiency and scalability of the reinforcement FL process in a distributed environment while ensuring data privacy and security through BT for IoMT applications. Results show that proposed approach achieve comparatively high reliability and outperforms the existing approaches.
Through the development of the metaverse concept from the Sumerian myth (5500 - 1800 BC) and mind-altering novel, “Snow Crash” in 1992, to today’s information age, human- and society-centred urban metaverse worlds, an extension of residents and urban society where the virtual and the physically real blend and are more organically integrated, are meant to mirror the fabric of urban life with no harm to their residents. The success of urban metaverse cybercommunities depends on the quality of data-driven Smart City (SC) Digital Twins (DTs), the seamless exchange of data between cyber and physical worlds (e.g. between residents and their counterpart “Avatars’’) and the processing of the data effectively and efficiently with no vicious interventions. The potential risks in this ecosystem that incorporates Web3 can be extremer than the ones in Web2 since users are immersed with multiple tightly coupled wearable sensor-rich devices perceiving the blend of the real and the virtual with possible imminent negative experiences. This study, by analysing potential cyberthreats in the urban metaverse cyberspaces, proposes a blockchain-based Decentralised Privacy-Preserving Machine Learning (DPPML) authentication and verification technique, which uses the metaverse immersive devices and can be instrumented effectively against identity impersonation and theft of credentials, identity, or avatars.
Yinqiu Liu, Hongyang Du, Dusit Niyato, Jiawen Kang · 8 authors
The rapid development of Artificial Intelligence- Generated Content (AIGC) has brought daunting challenges in the areas of service latency, security, and trustworthiness. Recently researchers have presented the edge AIGC paradigm, effectively optimizing the service latency by distributing AIGC services to edge devices. However, AIGC products are still unprotected and vulnerable to tampering and plagiarization. Moreover, as a kind of online non-fungible digital property, the free circulation of AIGC products is hindered by the lack of trustworthiness in open networks. For the first time, in this article, we present a blockchain-empowered framework to manage the lifecycle of edge AIGC products. Specifically, leveraging fraud proofability, we first propose a protocol to protect the ownership and copyright of AIGC, called Proof-of-AIGC. Then, we design an incentive mechanism to guarantee the legitimate and timely execution of the funds- AIGC ownership exchanges among anonymous users. Furthermore, we implement a multi-weight subjective logic-based reputation scheme with which AIGC producers can determine which edge service provider is trustworthy and can reliably handle their services. Through the use of test data, we demonstrate the superiority of the proposed approach. Last but not least, we discuss important open directions for further research.
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility.
To address the challenges of low credibility, difficult data sharing, and regulatory supervision issues involving electronic evidence storage in the judicial preservation process, this paper proposes a blockchain-based judicial evidence preservation scheme. The scheme utilizes the characteristics of blockchain's immutability to achieve credible forensics of electronic evidence on the chain and employs the decentralized storage of the interplanetary file system for secure and efficient off-chain storage. Simultaneously, it resolves the problem of declining throughput due to limited block capacity. Additionally, it leverages smart contract technology to encompass major aspects of the judicial process, including user case registration, authority management, judicial evidence uploading and downloading, case data sharing, partial disclosure of case information, and regulatory review. Simulation experiments demonstrate that the scheme significantly improves throughput and stability. Performance tests indicate that the transfer speed of the interplanetary file system can meet the data-sharing needs among judicial organizations.
Federated Learning (FL) has presented one of the radical paradigms that permit collaborative models to be trained without centralized sensitive data, thereby supporting both privacy and compliance-related concerns. Nevertheless, issues involving communication bottlenecks, non-iid data distribution, and privacy leak channels continue to impede its general use. This research project puts forward an integrated approach integrating FL with Agentic Intelligence (AIgI), forming a decentralized, adapting framework that integrates self-governing decision-making capacity, dynamic coordination and enhanced optimized resources. The framework utilizes sophisticated FL optimization techniques, such as FedYogi, to improve convergence rate and model precision and the Communication-Aware Federated Learning (CA-FL) technique to minimize the bandwidth usage. An assessment of privacy risks is performed with the help of the FedInverse tool, which shows vulnerabilities to model inversion attacks and supports the idea of multi-layered defense mechanisms. As experimental results confirm, the proposed framework achieves excellent accuracy compared to conventional FedAvg, both in terms of precision, recall, and Dice similarity coefficient and scales to thousands of clients thanks to FEDn orchestration. The integration of agentic reasoning enables the system to be adaptive enough to cope with a heterogeneous environment, the lack of synchronous involvement of clients, and the trade-off between training during the optimization of network conditions. The presented work forms the basis of a new generation of privacy-preserving, decentralized AI architecture that is resilient, scalable, and apt to be used in sensitive applications, like healthcare, finance, and critical infrastructure.
Ensuring authorized access control in the IoT is vital for privacy and safety protection. Our study presents the novel IHIBE framework, which combines IOTA (a distributed ledger technology) with hierarchical identity-based encryption (HIBE), thereby enhancing both IoT security and scalability. This approach secures access tokens and policies while reducing the computational demand on data owners. Our empirical findings reveal a significant performance gap, with access rights delegation on the Raspberry Pi 4 exceeding those on AWS by over 250%. Moreover, our analysis uncovers optimal identity policy depths: up to 640 identities on AWS and 640 on the Raspberry Pi 4 for systems with higher tolerable delays, and 320 identities on AWS versus 160 on the Raspberry Pi 4 for systems with lower tolerable delays. The system shows practical viability, exhibiting insignificant operational time differences compared to Zhang et al.'s schemes, particularly in access rights verification processes, with a minimal difference of 33.35%. Our extensive security assessment, encompassing scenarios like encrypted token theft and compromise of authority, affirms the efficacy of our challenge-response and last-word challenge (LWC) mechanisms. This study underscores the importance of platform choice in IoT system architectures and provides insights for deploying efficient, secure, and scalable IoT environments.
Considering the privacy challenges of secure storage and controlled flow, there is an urgent need to realize a decentralized ecosystem of private blockchain for cyberspace. A collaboration dilemma arises when the participants are self-interested and lack feedback of complete information. Traditional blockchains have similar faults, such as trustlessness, single-factor consensus, and heavily distributed ledger, preventing them from adapting to the heterogeneous and resource-constrained Internet of Things. In this paper, we develop the game-theoretic design of a two-sided rating with complete information feedback to stimulate collaborations for private blockchain. The design consists of an evolution strategy of the decision-making network and a computing power network for continuously verifiable proofs. We formulate the optimum rating and resource scheduling problems as two-stage iterative games between participants and leaders. We theoretically prove that the Stackelberg equilibrium exists and the group evolution is stable. Then, we propose a multi-stage evolution consensus with feedback on a block-accounting workload for metadata survival. To continuously validate a block, the metadata of the optimum rating, privacy, and proofs are extracted to store on a lightweight blockchain. Moreover, to increase resource utilization, surplus computing power is scheduled flexibly to enhance security by degrees. Finally, the evaluation results show the validity and efficiency of our model, thereby solving the collaboration dilemma in the private blockchain.