Blockchain technologies have introduced a compelling paradigm for a new understanding of security through decentralized networks and consensus mechanisms. However, they need all data to be public, which may be unacceptable for use cases such as biometric data processing or sensitive monetary transactions. Therefore, confidentiality is identified as a need in blockchain. Additionally, blockchain can contribute to confidential applications by providing publicly verifiable mechanisms, therefore enhancing security. This work presents a framework for cost-effective and publicly verifiable confidential computations in blockchain, by relying on secure multi-party computation committees and zero-knowledge proofs. Our framework supports arbitrary computations on confidential data enforced by smart contracts. Additionally, staking, incentives, and cheat identification are provided as solutions to enhance trust. We also provide a technical solution to embed secure multi-party computations within smart contracts by using the Promise programming pattern. Finally, a cost analysis is provided to justify the feasibility of the framework compared to other solutions.
Summary The proliferation of network devices capable of gathering, transmitting, and receiving data over the Internet has spurred the widespread adoption of Internet of Things (IoT) devices, particularly in resource‐oriented applications. Integrating blockchain, IoT, homomorphic encryption, and federated learning requires a balance between computational requirements and real‐time performance. Secure key management is crucial to maintain data privacy and integrity. Compliance with privacy regulations requires careful implementation of privacy‐preserving mechanisms in blockchain‐enabled IoT environments, which can be subjected to various attacks. Addressing these challenges requires interdisciplinary expertise, research, and innovation to develop more efficient and effective privacy‐preserving techniques tailored to the unique characteristics of such environments. This research introduces the Modified Homomorphic Encryption Federated‐based Adaptive Hybrid Dandelion Search (MHEF‐AHDS) algorithm as an effective framework to enhance security in blockchain‐enabled IoT systems. The amalgamation of Modified Homomorphic Encryption (MHE) and Federated Learning (FL) constitutes a potent alliance that addresses privacy concerns within collaborative and decentralized machine learning environments. This facilitates secure and adaptable data collaboration, effectively mitigating privacy risks associated with sensitive information. The integration of quantum machine learning into security applications presents an exciting opportunity for distinctive progress and innovation. Within this work, the Adaptive Hybrid Dandelion optimization algorithm, featuring an Initial search strategy, is employed for hyperparameter optimization thereby elevating the performances of the proposed MHEF‐AHDS method. Furthermore, the integration of smart contracts and Blockchain‐based IoT enhances the overall security of the proposed method. MHEF‐AHDS comprehensively tackles privacy, security, and scalability challenges through robust security measures and privacy enhancements. The performance evaluation of the MHEF‐AHDS method encompasses a thorough analysis based on key metrics such as throughput, latency, scalability, energy consumption, accuracy, precision, recall, and f1‐score. Comparative assessments against existing methods are conducted to gauge the effectiveness of the proposed method in addressing security, privacy, and scalability concerns.
In recent years, the confluence of federated learning and model parallelism has revolutionized the landscape of deep learning on distributed systems, significantly enhancing efficiency and scalability. Federated learning, a decentralized approach, enables multiple edge devices to collaboratively train a model without sharing their data, thereby preserving privacy and reducing latency. Model parallelism, on the other hand, divides a large model across several devices, allowing for simultaneous computation and faster processing. By synergizing these two paradigms, researchers have developed innovative frameworks that leverage the strengths of both approaches, achieving superior performance and resource utilization. This hybrid strategy addresses the limitations of traditional centralized training, offering a robust solution for large-scale, privacy-sensitive applications.The integration of federated learning and model parallelism not only optimizes computational resources but also mitigates communication bottlenecks inherent in distributed systems. This amalgamation is particularly advantageous for deep learning tasks involving vast datasets and complex models, as it distributes the computational load and enhances fault tolerance. Moreover, this approach supports continuous learning from distributed data sources, facilitating real-time updates and adaptability. As a result, turbocharged AI systems leveraging these technologies can efficiently handle the growing demands of contemporary deep learning applications, paving the way for advancements in fields such as healthcare, finance, and autonomous systems.
Web 3.0 represents the next significant evolution of the internet that embodies the underlying decentralized network architectures, distributed ledgers, and advanced AI capabilities. Though the technologies are maturing rapidly, considerable barriers exist to high-scale adoption. The author discusses the barriers and the mitigations through specific technologies maturing to solve those issues in an earlier paper titled Moving Beyond POCs and Pilots, published in 2023 in Blockchain in Healthcare Today. These include privacy-preserving technologies, off-chain and on-chain design optimizations, and the multi-dimensional approach needed in planning and adopting these technologies. As an extension, this paper discusses one such enabler, zero knowledge machine learning (ZKML), which merges two streams of technology in unique ways to address problems in privacy and the cost of inference. Zero-knowledge proofs (ZKP) allow one party to prove the validity of a statement to another party without revealing any additional information about the statement itself. The ZKML combines the cryptographic principle of ZKP with machine learning (ML) techniques. It is still a maturing technology and needs baselines for applications in global healthcare. In this effort, the authors conceptualize the technical and operational feasibility of using ZKML and implement a reference healthcare implementation using the synthetic International Consortium for Health Outcomes Measurement (ICHOM) in the evaluation phase in a global healthcare setting for high-volume data collection, including patient-reported outcomes. Model complexity reduction is researched and reported for the ICHOM diabetes dataset to advance the usage of ML models in global standards of healthcare data collection in network decentralized architectures for increased data protection and efficiencies.
The combination of blockchain technology with federated learning (FL) introduces an innovative method to improve security, privacy, and trust in decentralized machine learning systems. Federated learning allows for distributed model training while safeguarding data privacy by keeping original data on local devices. Nonetheless, it confronts issues such as ensuring data integrity, the reliability of model updates, and vulnerability to adversarial attacks. Blockchain technology creates an immutable, decentralized ledger that guarantees transparency, secure aggregation, and verifiable updates to models. By utilizing blockchain's consensus protocols, smart contracts, and cryptographic methods, FL can counteract threats like poisoning attacks and eliminate single points of failure. This paper examines the architectural framework, advantages, and challenges of merging blockchain with FL, in addition to potential enhancements to boost scalability and efficiency. We also emphasize practical applications and prospective research pathways in this evolving field.
The emergence of cryptocurrencies as a form of digital payments has contributed to the emergence of numerous opportunities for the implementation of effective and efficient financial transactions, however, new fraud and money laundering schemes have emerged, as the anonymity and decentralization inherent in cryptocurrencies complicate the process of monitoring transactions and control by governments and law enforcement agencies. This study aims to develop a mechanism for analyzing transactions in the Ethereum cryptocurrency using a Bayesian classifier to identify potentially suspicious transactions that may be related to terrorist financing and money laundering. The Bayesian approach makes it possible to consider the probabilistic characteristics of transactions and their interrelationships to increase the accuracy of detecting anomalous and potentially illegal transactions. For the analysis, data on transactions of the Ethereum currency from June 2020 to December 2022 were taken. The developed mechanism involves determining a set of characteristics of transaction graph nodes that identify the potential for their use in illegal financial transactions and forming intervals of their permissible values. The article presents cryptocurrency transactions as an oriented graph, with the nodes being the entities conducting transactions and the arcs being the transactions between the nodes. In assessing the risks of using cryptocurrencies in money laundering, the number/amount of transactions to and from the respective node, the balance of these transactions (absolute value), and the type of node were considered. The analysis showed that among the 100 largest nodes in the network, 11 were identified as having a «critical» risk level, and the most closely connected nodes were identified. This methodology can be used not only to analyze the Ethereum cryptocurrency but also for other cryptocurrencies and similar networks.
Smart grids offer promising opportunities for energy efficiency but raise concerns about data privacy and transparency. This paper presents a new framework for Secure and Auditable Private Data Sharing designed specifically for smart grids, with a focus on its functionality as a password manager. By utilizing smart contracts and blockchain technology, the framework creates explicit guidelines for data usage that guarantee clear oversight of data access and intended uses. In addition to managing smart grid data, the framework securely stores passwords and credentials, providing users with a comprehensive solution for data security and access management. The use of off-chain smart contract execution and trusted execution environments maximizes performance. To further assure transactional reliability during the result dissemination and payment procedures, the framework employs a two-phase atomic delivery mechanism. Energy service providers gain from optimum contracts because they encourage high-quality data exchange and customer participation through the use of contract theory. Extensive simulations validate the efficacy of the proposed approach, enabling users to securely access their data remotely through a cloud platform integrated with open-source services.
Data aggregation management is paramount in data-driven distributed systems. Conventional solutions premised on centralized networks grapple with security challenges concerning authenticity, confidentiality, integrity, and privacy. Recently, distributed ledger technology has gained popularity for its decentralized nature to facilitate overcoming these challenges. Nevertheless, insufficient identity management introduces risks like impersonation and unauthorized access. In this paper, we propose Degator, a data aggregation management framework that leverages self-sovereign identity and functions in decentralized networks to address security concerns and mitigate identity-related risks. We formulate fully decentralized aggregation protocols for data persistence and acquisition in Degator. Degator is compatible with existing data persistence methods, and supports cost-effective data acquisition minimizing dependency on distributed ledgers. We also conduct a formal analysis to elucidate the mechanism of Degator to tackle current security challenges in conventional data aggregation management. Furthermore, we showcase the applicability of Degator through its application in the management of decentralized neuroscience data aggregation and demonstrate its scalability via performance evaluation.
Differential Privacy (DP) stands as a secure and efficient mechanism for privacy preservation, offering enhanced data utility without compromising computational complexity. Its adaptability is evidenced by its integration into blockchain-based Internet of Things (IoT) contexts, including smart wearables, smart homes, etc. Nevertheless, a notable vulnerability surfaces in decentralized environments where existing DP mechanisms falter in withstanding collusion attacks. This vulnerability stems from the absence of an efficient strategy to synchronize the privacy budget consumption and historical query information among all network participants. Adversaries can exploit this weakness, collaborating to inject a substantial volume of queries simultaneously into disparate blockchain nodes to extract more precise results. To address this issue, we propose a novel dual response DP mechanism to preserve privacy in blockchain-based IoT scenarios. It encompasses both direct and indirect response strategies, enabling an adaptive response to external queries, aiming to provide better data utility while preserving privacy. Additionally, this mechanism can synchronize historical query information and privacy budget consumption within the blockchain network to prevent privacy leakage. We employ Relative Error (RE), Mean Square Error (MSE), and privacy budget consumption as evaluation metrics to measure the performance of the proposed mechanism. Experimental outcomes substantiate that the proposed mechanism can adapt to blockchain networks well, affirming its capacity for privacy and great utility.
Condorcet voting is widely regarded as one of the most important voting systems in social choice theory. However, it has seen little adoption in practice, due to complex tallying and the need to break ties when there is a Condorcet cycle. Several online Condorcet voting systems have been developed to perform digital tallying and tie-breaking procedures, but they require voters to completely trust the server. Additionally, many end-to-end (E2E) verifiable e-voting systems require trustworthy authorities to perform complex decryption and tallying operations. We propose VERICONDOR, the first E2E verifibbolable Condorcet e-voting system without tallying authorities. VERICONDOR allows a voter to fully verify the tallying integrity by themselves while providing strong protection of ballot secrecy. We present novel zero-knowledge proof techniques to prove the well-formedness of an encrypted ballot with exceptional efficiency. VERICONDOR supports ranking candidates with strict preference, as well as indifference. The computational cost is exceptionally efficient for strict preferences at \(\mathcal{O}(n^{2})\) per ballot for \(n\) candidates, while remaining practical for indifferences at \(\mathcal{O}(n^{3})\) . In the case of ties, we show how to apply known Condorcet methods to break them in a publicly verifiable manner. Finally, we present a proof of concept implementation and evaluate its performance.
Alec Wells, Norbert Dajnowski, Aminu Bello Usman, John Murray · 5 authors
This paper presents a novel framework for one-to-many biometric systems by adapting decentralised storage over a centralised database solution, by leveraging smart contracts to address the concerns commonly associated with decentralised solutions. Smart contracts enforce strict privacy controls, enabling individuals to retain ownership and control over their biometric data on decentralised networks, while facilitating secure and efficient authentication, helping achieve the principles laid out by privacy by design. Biometric systems play a crucial role in identity verification and access control, but their deployment raises significant privacy challenges due to the sensitive nature of biometric data. Traditional approaches often involve centralised storage of biometric information, increasing the risk of data breaches and unauthorised access. We discuss the architecture, implementation, and benefits of our framework, highlighting its potential to enhance privacy and trust in one-to-many biometric systems across various applications.
As the next generation of the internet, Web 3.0 is expected to revolutionize the Internet and enable users to have greater control over their data and privacy. Federated learning (FL) enables data to be usable yet invisible during its use, thereby facilitating the transfer of data ownership and value. However, the issues of data size and blockchain computing power are of paramount importance for FL in Web 3.0. Due to the openness of Web 3.0, individuals can freely join or leave training and adjust data size, creating population uncertainty and making it difficult to design incentive mechanisms. Therefore, we propose a Poisson game-based FL incentive mechanism that motivates participants to contribute more data and computing power, considering the variability of data size and computing power requirements, and provides a feasible solution to the uncertainty of the number of participants using a Poisson game model. Additionally, our proposed FL architecture in Web 3.0 integrates FL with Decentralized Autonomous Organizations (DAO), utilizing smart contracts for contribution calculation and revenue distribution. This enables an open, free, and autonomous federated learning environment. Experimental evaluation shows that our incentive mechanism is feasible in blockchain with efficiency, robustness, and low overhead.
In the era emphasizing the privacy of personal data, verifiable federated learning has garnered significant attention as a machine learning approach to safeguard user privacy while simultaneously validating aggregated result. However, there are some unresolved issues when deploying verifiable federated learning in edge computing. Due to the constraint resources, edge computing demands cost saving measurements in model training such as model pruning. Unfortunately, there is currently no protocol capable of enabling users to verify pruning results. Therefore, in this paper, we introduce PrVFL, a verifiable federated learning framework that supports model pruning verification and heterogeneous edge computing. In this scheme, we innovatively utilize zero-knowledge range proof protocol to achieve pruning result verification. Additionally, we first propose a heterogeneous delayed verification scheme supporting the validation of aggregated result for pruned heterogeneous edge models. Addressing the prevalent scenario of performance-heterogeneous edge clients, our scheme empowers each edge user to autonomously choose the desired pruning ratio for each training round based on their specific performance. By employing a global residual model, we ensure that every parameter has an opportunity for training. The extensive experimental results demonstrate the practical performance of our proposed scheme.
Tatami puzzles are pencil puzzles with an objective to partition a rectangular grid into rectangular regions such that no four regions share a corner point, as well as satisfying other constraints. In this paper, we develop a physical card-based protocol called Tatami printer that can help verify solutions of Tatami puzzles. We then use the Tatami printer to construct zero-knowledge proof protocols for two such puzzles: Tatamibari and Square Jam. These protocols enable a prover to show a verifier the existence of the puzzles' solutions without revealing them.
Mingming Cui, Dezhi Han, Han Liu, Kuan‐Ching Li · 9 authors
Data sharing in Vehicular Social Networks (VSNs) is an essential road service that assists vehicle driving and promotes intelligent transportation applications. In VSNs, vehicles regularly collect and upload valuable data to share with other vehicles. Data encryption can be employed during data uploading and sharing to prevent malicious tampering and privacy disclosure. However, existing data-sharing schemes lack security, have high overhead in obtaining decrypted data, and show low trust in the central authority controlling the entire network. To facilitate data sharing in VSNs, this paper proposes a new scheme using consortium blockchain to realize secure data sharing. Nodes in the blockchain invoke smart contracts and implement the location-based Speculative Byzantine Fault Tolerance (LSBFT) to accomplish data-sharing transactions among vehicles. The scheme not only ensures the security of vehicle information but also protects the privacy of the shared data. Security analysis demonstrates that the proposed scheme can resist attacks and has shown transaction fairness, data confidentiality, non-repudiation, and traceability. Simulation results show that the scheme has higher sharing efficiency and less time to reach a consensus in the data storage process.
This article addresses critical health data integrity by proposing an HF (Hyperledger Fabric)-based architecture with integration into the global health data architecture based on distributed content-addressable storage networks.
Crowd-sensing has emerged as a powerful data retrieval model, enabling diverse applications by leveraging active user participation. However, data availability and privacy concerns pose significant challenges. Traditional methods like data encryption and anonymization, while essential, may not fully address these issues. For instance, in sparsely populated areas, anonymized data can still be traced back to individual users. Additionally, the volume of data generated by users can reveal their identities. To develop credible crowd-sensing systems, data must be anonymized, aggregated and separated into uniformly sized chunks. Furthermore, decentralizing the data management process, rather than relying on a single server, can enhance security and trust. This paper proposes a system utilizing smart contracts and blockchain technologies to manage crowd-sensing campaigns. The smart contract handles user subscriptions, data encryption, and decentralized storage, creating a secure data marketplace. Incentive policies within the smart contract encourage user participation and data diversity. Simulation results confirm the system's viability, highlighting the importance of user participation for data credibility and the impact of geographical data scarcity on rewards. This approach aims to balance data origin and reduce cheating risks.
Blockchain technology (BT) offers a secure and transparent method for recording and verifying transactions. Unlike traditional systems reliant on central authorities, BT utilizes a decentralized network of participants to collectively validate and maintain a tamper-proof digital ledger. This ledger, comprised of cryptographically linked blocks containing transaction data, ensures immutability and resistance to manipulation. Secured by cryptographic algorithms and supported by consensus mechanisms like proof of work or proof of stake, BT fosters trust and transparency. However, India, like many nations, faces evolving legal concerns regarding data protection as BT adoption grows.
Aug 19, 2024·2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics
Smart cities, through investment in human and social capital as well as traditional and modern communications infrastructure, drive sustainable economic growth and high quality of life. IoT devices, as crucial components of smart cities, leverage the Federated Learning (FL) paradigm to contribute significantly to the advancement of smart cities by providing the value of data while safeguarding local data. However, existing federated learning frameworks for smart cities face several issues, including excessive centralization of decision-making and data, unbalanced resource allocation, and security and privacy concerns. To address these challenges, This paper aims to propose a blockchain-based federated learning framework for IoT devices in smart cities. We combine multiple security and privacy-preserving techniques such as differential privacy and Trusted Execution Environments (TEE) with federated learning to establish a framework that facilitates secure and trustworthy data exchange and FL requirements for IoT devices in smart cities. Moreover, we offload the local training computation from the limited IoT devices to the edge server and execute trust aggregation on the blockchain smart contracts. We build a prototype of our designed system and conduct experiments on diverse federated learning datasets. Experimental results demonstrate that our scheme achieves high efficiency while ensuring security and privacy. Through this work, we provide a viable solution for federated learning in smart cities, thereby advancing the sustainable development and intelligence of smart cities.
Federated Learning (FL) has become a transformative approach in machine learning, allowing decentralized training of models across multiple devices while preserving data privacy. This paradigm addresses critical concerns related to data privacy, security, and communication overhead, making it particularly relevant for applications in domains such as healthcare, finance, and the Internet of Things (IoT). Resource-constrained FL extends this concept to environments where computational, communication, and energy resources are limited, such as edge networks and IoT devices. This extension focuses on optimizing various aspects of the learning process to enable effective model training even in resource-limited settings. The primary aim of this survey is to provide a comprehensive and structured overview of the current state of research in FL and resource-constrained FL. By examining 62 key publications, this survey synthesizes insights and developments across these domains, highlighting advancements, challenges, and gaps that exist. This survey aims to provide a holistic view of the advancements and ongoing challenges in FL and resource-constrained FL. It identifies research gaps and proposes future directions, such as improving communication efficiency, developing adaptive learning algorithms, and enhancing resource management strategies. This survey serves as a valuable resource for researchers, practitioners, and stakeholders in the field, offering practical insights and guiding future exploration and innovation in FL and its applications in resource-constrained environments.
Federated Learning is a form of privacy-preserving machine learning where multiple entities train local models, which are then aggregated into a global model. Current forms of federated learning rely on a centralized server to orchestrate the process, leading to issues such as requiring trust in the orchestrator, the necessity of a middleman, and a single point of failure. Blockchains provide a way to record information on a transparent, distributed ledger that is accessible and verifiable by any entity. We leverage these properties of blockchains to produce a decentralized, federated learning marketplace-style protocol for training models collaboratively. Our core contributions are as follows: first, we introduce novel staking, incentivization, and penalization mechanisms to deter malicious nodes and encourage benign behavior. Second, we introduce a dual-layered validation mechanism to ensure the authenticity of the models trained. Third, we test different components of our system to verify sufficient incentivization, penalization, and resistance to malicious attacks.