Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning (BSAFL) framework based on dual aggregation for asynchronous cross-domain federated learning scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.
Ethereum 2.0 is a major upgrade to improve its scalability, throughput, and security. In this version, RANDAO is the scheme to randomly select the users who propose, confirm blocks, and get rewards. However, a vulnerability, referred to as the `Last Revealer Attack' (LRA), compromises the randomness of this scheme by introducing bias to the Random Number Generator (RNG) process. This vulnerability is first clarified again in this study. After that, we propose a Shamir's Secret Sharing (SSS)-based RANDAO scheme to mitigate the LRA. Through our analysis, the proposed method can prevent the LRA under favorable network conditions.
Priced oblivious transfer (POT) is a cryptographic protocol designed for privacy-preserving e-commerce of digital content. It involves two parties: the merchant, who provides a set of priced items as input, and a customer, who acquires one of them. After the protocol has run, the customer obtains the item they chose, while the merchant cannot determine which one. Moreover, the protocol guarantees that the customer gets the content only if they have paid the price established by the merchant. In a recent paper, the authors proposed a POT system where the payments employed e-coin transactions. The strong point of the proposal was the absence of zero-knowledge proofs required in preceding systems to guarantee the correctness of payments. In this paper, we propose a novel e-coin-based POT system with a fast item retrieval procedure whose running time does not depend on the number of items for sale. This is an improvement over the aforementioned existing proposal whose execution time becomes prohibitively long when the catalog is extensive. The use of zero-knowledge proofs is neither required.
The advent of the digital era and computer-based remote communications has significantly enhanced the applicability of various sciences over the past two decades, notably data science (DS) and cryptography (CG). Data science involves clustering and categorizing unstructured data, while cryptography ensures security and privacy aspects. Despite certain CG laws and requirements mandating fully randomized or pseudonoise outputs from CG primitives and schemes, it appears that CG policies might impede data scientists from working on ciphers or analyzing information systems supporting security and privacy services. However, this study posits that CG does not entirely preclude data scientists from operating in the presence of ciphers, as there are several examples of successful collaborations, including homomorphic encryption schemes, searchable encryption algorithms, secret-sharing protocols, and protocols offering conditional privacy. These instances, along with others, indicate numerous potential solutions for fostering collaboration between DS and CG. Therefore, this study classifies the challenges faced by DS and CG into three distinct groups: challenging problems (which can be conditionally solved and are currently available to use; e.g., using secret sharing protocols, zero-knowledge proofs, partial homomorphic encryption algorithms, etc.), open problems (where proofs to solve exist but remain unsolved and is now considered as open problems; e.g., proposing efficient functional encryption algorithm, fully homomorphic encryption scheme, etc.), and hard problems (infeasible to solve with current knowledge and tools). Ultimately, the paper will address specific solutions and outline future directions to tackle the challenges arising at the intersection of DS and CG, such as providing specific access for DS experts in secret-sharing algorithms, assigning data index dimensions to DS experts in ultra-dimension encryption algorithms, defining some functional keys in functional encryption schemes for DS experts, and giving limited shares of data to them for analytics.
Blockchain technology is a decentralized and secure paradigm for data processing, sharing, and storing. It relies on consensus protocol for all decisions, which focuses on computational and resource capability. For example, proof of work (PoW) and proof of stake (PoS) are the most famous consensus protocols that are currently used. However, these current consensus protocols are required to recruit a node with a high computational or a large amount of cryptocurrency to act as a miner node and to generate a new block. Unfortunately, these PoW and PoS protocols could be impractical for adoption in today’s technological fields, such as the Internet of Things and healthcare. In addition, these protocols are susceptible to flexibility, security, and fairness issues, as they are discussed in detail in this work. Therefore, this paper introduces a proof of fairness (PoF) as a dynamic and secure consensus protocol for enhancing the mining selection process. The selection of the miner node is influenced by numerous factors, including the time required to generate a block based on the transaction’s sensitivity. Firstly, a reverse auction mechanism is designed as an incentive mechanism to encourage all nodes to participate in the miner selection process. In a reverse auction, each node will draw its strategy based on its computational capability and claimed cost. Secondly, an expressive language is developed to categorize transaction types based on their sensitivity to processing time, ensuring compatibility with our miner selection process. Thirdly, a homomorphic concept is designed as a security and privacy scheme to protect the bidder’s data confidentiality. Finally, an extensive evaluation involving numerical analysis was carried out to assess the efficiency of the suggested PoF protocol, which confirms that the proposed PoF is dynamic and more efficient than current PoW and PoS consensus protocols.
Abstract To meet the demand for high‐quality healthcare services, data trading can effectively promote the circulation of medical data and improve the level of healthcare services. To address the existing problems of data regulation difficulties and data privacy leakage in medical data trading, a trusted and regulated data trading scheme based on blockchain and zero‐knowledge proof is proposed. In this scheme, a regulatory institution is introduced to control the issuance of authorized tokens and ensure the controllability of data sharing activities. The blockchain takes over the task of generating public parameters to reduce the computational overhead of the system. Based on homomorphic proxy re‐encryption technology, users can perform data analysis in the cloud to ensure data security. Smart contracts and zero‐knowledge proof technology can automatically verify the validity of data to protect the rights and interests of data users; at the same time, efficient consensus algorithms can also increase the rate of transactions processed by the blockchain system. Finally, as the security and performance analysis shows, the scheme in this paper has better security, higher efficiency and more comprehensive functions.
Lakshmi Rama Kiran Pasumarthy, Hisham Ali, William J. Buchanan, Jawad Ahmad · 7 authors
There is an increasing need to share threat information for the prevention of widespread cyber-attacks. While threat-related information sharing can be conducted through traditional information exchange methods, such as email communications etc., these methods are often weak in terms of their trustworthiness and privacy. Additionally, the absence of a trust infrastructure between different information-sharing domains also poses significant challenges. These challenges include redactment of information, the Right-to-be-forgotten, and access control to the information-sharing elements. These access issues could be related to time bounds, the trusted deletion of data, and the location of accesses. This paper presents an abstraction of a trusted information-sharing process which integrates Attribute-Based Encryption (ABE), Homomorphic Encryption (HE) and Zero Knowledge Proof (ZKP) integrated into a permissioned ledger, specifically Hyperledger Fabric (HLF). It then provides a protocol exchange between two threat-sharing agents that share encrypted messages through a trusted channel. This trusted channel can only be accessed by those trusted in the sharing and could be enabled for each data-sharing element or set up for long-term sharing.
Se Elnour, William J. Buchanan, Paul Keating, Mwrwan Abubakar · 5 authors
The vSPACE experimental proof-of-concept (PoC) on the TrueElect[Anon][Creds] protocol presents a novel approach to secure, private, and scalable elections, extending the TrueElect and ElectAnon protocols with the integration of AnonCreds SSI (Self-Sovereign Identity). Such a protocol PoC is situated within a Zero-Trust Architecture (ZTA) and leverages confidential computing, continuous authentication, multi-party computation (MPC), and well-architected framework (WAF) principles to address the challenges of cybersecurity, privacy, and trust over IP (ToIP) protection. Employing a Kubernetes confidential cluster within an Enterprise-Scale Landing Zone (ESLZ), vSPACE integrates Distributed Ledger Technology (DLT) for immutable and certifiable audit trails. The Infrastructure as Code (IaC) model ensures rapid deployment, consistent management, and adherence to security standards, making vSPACE a future-proof solution for digital voting systems.
Jingxue Chen, Eric Wang, Gautam Srivastava, Turki Ali Alghamdi · 7 authors
The space-air-ground-sea three-dimensional (3D) network is a comprehensive communication network system. This 3D network combines the extensive coverage of satellite communications, the adaptability of unmanned aerial vehicle (UAV) communications, the reliability of terrestrial communications, and the necessity of maritime communications. These networks generate enormous amounts of data, and training machine learning (ML) models on this data will have a significant impact on the industry. At the same time, the availability of such data poses numerous security threats, which can be overcome by Federated Learning (FL). The decentralized training in FL can provide a universal model from local data generated by the 3D network However, most existing FL frameworks have a centralized server, which questions the credibility, single-point failure, and global confidence. To solve these problems, industrial blockchain technology has received much attention by replacing centralized servers in traditional FL, which offers a promising approach to address key issues such as data privacy and security. In a blockchain-based system, digital signature is the core component for ensuring data integrity and system security, however, private key disclosure can pose significant risks. The security can be enhanced by using threshold signature, which provides a more reliable foundation for FL by storing keys in multiple nodes and requiring multiple nodes to collaborate to generate signatures. In this paper, we propose a Threshold signing scheme for ISO/IEC Digital Signature Standards (TDSS) in industrial blockchain. The TDSS scheme helps FL to achieve truly distributed decentralization for unified space-air-ground-sea model training. The TDSS scheme exploits the SM-2 digital signature algorithm in the ISO/IEC standard when t out of n nodes in industrial blockchain interact with each other to calculate the signature. The experimental results and analyses show that the TDSS scheme has provable security and efficient against security attacks, which can be applied to large-scale threshold signing scenarios.
The exponential advancement of quantum computing poses unprecedented threats to conventional cryptographic systems employed in banking infrastructure. This paper presents a comprehensive quantum-enhanced cryptographic framework that integrates post-quantum algorithms with distributed ledger technologies to establish resilient financial security systems. Through systematic analysis of lattice-based cryptography, hash-based signatures, and multivariate cryptographic schemes, this research demonstrates significant improvements in computational security while maintaining operational efficiency. The proposed framework achieves 99.7% security resilience against quantum attacks while reducing transaction processing overhead by 23% compared to traditional RSA-based systems. Experimental validation across simulated banking environments reveals enhanced performance metrics in key generation, digital signatures, and secure communication protocols. The integration of quantum-resistant algorithms with blockchain-based consensus mechanisms provides a robust foundation for future-proof banking security architectures.
Abstract Popular Secp256k1 and Schnorr algorithms offer strong security in current Blockchains. However, they are vulnerable to quantum attacks. To solve this problem, several quantum-resistant algorithms have been proposed. However, the performance evaluations and tangible analyses of these algorithms on current Blockchains have not been studied yet. In this context, a performance analysis of quantum-resistant algorithms on a Blockchain can provide valuable insight into the efficiency of quantum-resistant algorithms in real-world scenarios. To address this need, we prototyped and analyzed a quantum-resistant Blockchain using the Falcon algorithm. Falcon is selected because it provides smaller signature and key size compared to Crystals-Dilithium and Sphincs+. We then measured in real-time the key size, transaction signature size, and transaction verification time. The paper also discusses the potential scalability limitations of the proposed quantum-resistant Blockchain and suggests an approach to select quantum-resistant algorithms based on different Blockchain use cases. Our approach and benchmark results have implications for the future development and adoption of quantum-resistant Blockchains.
Blockchain (BC) is the type of distributed ledger technology that consists of a growing list of records, called blocks, that are securely linked together using cryptography. Each BC-based solution deploys a specific consensus algorithm that guarantees the consistency of the ledger over time. The most famous, and yet claimed to be the most secure, is the Proof-of-Work (PoW) consensus algorithm. In this paper, we revisit the fundamental calculations and assumptions of this algorithm, originally presented in the Bitcoin white paper. We break down its claimed calculations, in order to better understand the underlying assumptions of the proposal. We also propose a novel formalization model of the PoW mining problem using the Birthday paradox. We utilize this model to formalize and analyze Partial Pre-Image attacks on PoW-based BCs, with formal analysis that confirmed the experimental results and the previously proposed implications. We build on those analyses and propose new concepts for benchmarking the security of PoW-based systems, including Critical Difficulty and Critical Difficulty per given portion. Our calculations resulted in several important findings including the profitability of launching Partial Pre-Image attacks on PoW-based BCs, once the mining puzzle difficulty reaches a given threshold. Specifically, for any compromised portion of the network (q<0.5; honest majority assumption still holds), the attack was formally proven profitable, once the PoW mining puzzle difficulty reaches 56 leading zeros.
Bo Zhang, Tao Zhang, Zesheng Xi, Ping Chen · 6 authors
With the rapid development of the Internet of Things (IoT), ensuring secure communication between devices has become a crucial challenge. This paper proposes a novel secure communication solution by extracting wireless channel state information (CSI) features from IoT devices to generate a device identity. Due to the instability of the wireless channel, the CSI features are fuzzy and time-varying; thus, we a employ locally sensitive hashing (LSH) algorithm to ensure the stability of the generated identity in a dynamically changing wireless channel environment. Furthermore, zero-knowledge proofs are utilized to guarantee the authenticity and effectiveness of the generated identity. Finally, the identity generated using the aforementioned approach is integrated into an IBE communication scheme, which involves the fuzzy extraction of channel state information from IoT devices, stable identity extraction for fuzzy IoT devices using LSH, and the use of zero-knowledge proofs to ensure the authenticity of the generated identity. This identity is then employed as the identity information in identity-based encryption (IBE), constructing the device’s public key for achieving confidential communication between devices.
BACKGROUND: Ensuring the security and privacy of patient data is a critical concern in the healthcare industry. The growing utilization of electronic data transmission and storage in medical records has amplified apprehensions about data security. However, due to varying stakeholder interests, not all data can be freely shared, necessitating the development of secure protocols. MATERIALS AND METHODS: This study presents a highly secure protocol that integrates blockchain technology, patient biometric information, and robust cryptographic algorithms (elliptic curve cryptography (ECC) and advanced encryption algorithm (AEC)) to facilitate data encryption and decryption. The protocol encompasses secure login, secure key sharing, and data sharing mechanisms among miners, offering comprehensive security measures. To validate the effectiveness of the proposed protocol, both informal and formal security analyses are conducted. The security protocol description language in Scyther is utilized to evaluate the protocol's resilience against attacks. RESULTS: The culmination of this research is a secure protocol that leverages blockchain technology and ECC for the secure storage and sharing of medical records. The protocol covers all stages, including system setup, user registration, login mechanisms, key exchange between users and blockchain, communication between blockchains, and interaction with other miners, with a steadfast emphasis on security. Furthermore, the protocol's communication and computation costs are assessed, with a comparison to existing blockchain-based schemes. Informal proofs establish the protocol's security against common attacks faced by medical institutions. Formal simulation of the protocol using the Scyther tool provides definitive evidence of its resistance to attacks. CONCLUSIONS: As a result, this protocol presents a viable real-time implementation solution for safeguarding patient data within the healthcare domain, representing a significant contribution to data security.
Engin Zeydan, Josep Mangues‐Bafalluy, Şuayb S. Arslan, Yekta Türk
Identity and access management frameworks address user access rights and data governance for organizations, vendors and users. In response to the problems associated with centralized authorities (e.g. single point of failure , limited scalability, lack of user control), new identity management models have emerged, such as Self-Sovereign Identity (SSI), which relies on verifiable data registers to validate Decentralized Identifier (DIDs) and can be achieved in many different ways, e.g. through Distributed Ledger Technology (DLT), distributed databases or other decentralized systems. The main goal of SSI is to enable users to take control of managing their data shared with different services. In this paper, we examine a possible application of the SSI concept to aerial base station (ABS)- integrated networks. The paper presents the effective use of DID implementation to provide a secure and decentralized way to create, associate and verify credentials and identities of ABSs, ensuring secure communication between Ground Base stations (GBSs) and other nodes in the network in a multi-operator scenario. In the numerical results, the average values of various metrics (namely, the average credential presentation time, the average credential offer time, the average DIDcomm connection creation time, the average DIDcomm signing time, and the average DIDcomm revoke credential time) related to credential operations in a DID management system are given for three different number of requests (50 K, 75 K, and 100 K). We have also provided the values of the different status codes that occurred in 100 K operations in the same DID management system. Towards the end of the paper, a comparison is made between SSI-based and Non-fungible token (NFT)-based blockchain solutions, also discussing the challenges and future directions of SSI solutions in the context of ABS-integrated networks.
Статья посвящена актуальной проблеме обеспечения конфиденциальности в системах распределенного реестра. Рассматривается прикладная задача обеспечения конфиденциальности данных при операциях с цифровыми финансовыми активами. Представлено сравнение различных методов обеспечения конфиденциальности, включая перемешивающие сети, кольцевые подписи и оффчейн-протоколы. Отмечено, что эти методы не достигают достаточного уровня децентрализации, что является важным аспектом для систем распределенного реестра. Для одновременного обеспечения свойств децентрализации и конфиденциальности информации используются методы доказательства с нулевым разглашением, включая методы компактных неинтерактивных доказательств знания (SNARK). В статье приводится математическая модель систем доказательства SNARK, а также описаны подходы к их программной реализации. Приведены результаты экспериментов, направленные на сравнение производительности методов SNARK для решения прикладной задачи проведения операций с цифровыми финансовыми активами. Результаты эксперимента позволяют выделить дальнейшие возможности снижения времени генерации доказательства и сокращения его объема посредством использования пакетной верификации. Полученные результаты имеют практическую значимость для разработки систем распределенного реестра, требующих высокого уровня конфиденциальности и децентрализации.
Integrating blockchain technology with the Internet of Things (IoT) facilitates seamless interaction between IoT devices and systems to securely share, access, and exchange data. However, ensuring adequate access control within blockchain-enabled IoT (BIoT) systems remains a significant challenge. It is often difficult to adapt existing access control mechanisms to the dynamic and context-dependent nature of IoT environments, necessitating a robust context-aware approach to ensure adequate security and the privacy of resources within BIoT systems. In this paper, we propose a novel smart contract-enabled context-aware access control (SC-CAAC) scheme for BIoT systems. It utilizes context-aware access control models that consider contextual information, including user profile, purpose, date, time, location, resource, and operating environment specifications, to make access control decisions. Smart contracts dynamically enforce access control policies and manage access permissions, ensuring that sensitive data and resources are accessible only to authorized users. The proposed scheme leverages the immutability, transparency, and decentralization of a blockchain that is shared by multiple participants in a consortium network, removing the need for a central authority to record and audit access control policies and decisions and promoting accountability and trust. The implementation and evaluation of our proposed scheme using the Hyperledger Besu blockchain demonstrates its effectiveness and scalability in real-world scenarios.
Nowadays, the integration of blockchain technology with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has drawn the researcher attention because it can provide key security auditing and transaction traceability in the context of data sharing. However, in a majority of existing blockchain-based CP-ABE schemes, private keys were still issued by one central authority that would lead to heavy computation, higher transaction costs, and restricted scalability within the decentralized system. To address these challenges, we present an enhancement approach towards utilizing distributed key management service (KMS) and zero-knowledge paradigms. In our improved novel blockchain system model, we define two types of blockchain nodes for the CP-ABE scheme through staking mechanism. Firstly, the proxy re-encryption nodes are introduced to offer secure multi-party management and distribution of the CP-ABE's master secret key, eliminating dependence on a central authority and producing proofs of re-encryption correctness. Secondly, the operator nodes can collect all transactional information in blockchain-based CP-ABE scheme and then send the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) proofs to verify the batch’s integrity via smart contract. Subsequently, we employ the staking economic incentive model with reward determination and slashing in the decentralized blockchain system to ensure network security. Finally, simulation results validate the effectiveness of our proposed scheme in achieving secure and efficient data sharing. Even amidst the pressure of 100 simultaneous transactions, the average response time for a single node remains at an approximate 28 s. Additionally, there is a notable decrease in on-chain gas consumption, with a gas reduction exceeding 61%. Comparative analyses further indicate that our blockchain-based CP-ABE scheme, in conjunction with a decentralized KMS, offers a superior balance between computational efficiency and functional capability.
Yustus Eko Oktian, Thi-Thu-Huong Le, Uk Jo, Agus Mahardika Ari Laksmono · 5 authors
With the increasing number of Internet of Things devices, it is crucial to keep them up-to-date to prevent cyber attacks. Traditional centralized delivery is not suitable for scaling and also can be too expensive to run for small vendors. Thus, finding a fully secure, scalable, yet cost-efficient firmware update distribution strategy is always an open research problem. This paper tries to answer those problems by proposing Patchman, a secure decentralized firmware update delivery service for the Internet of Things ecosystem leveraging blockchain. When a new firmware patch is available, vendors make a bid in the smart contract for anyone to join as firmware distributors. For each successful delivery to targeted devices, distributors are rewarded with tokens. Meanwhile, devices gain a reputation score every time they successfully install an update. To ensure robustness and fairness, we develop secure fair exchange protocols using verifiable proof-of-delivery and proof-of-installation. Those proofs can be traded in the blockchain for rewards and reputation scores increase, proving that the proof-holders have successfully processed a firmware delivery and firmware installation task. This way, the firmware update delivery can be executed safely without centralized third-party control. Our evaluation results show that our implementation complies with the five security goals that we envision. We also have successfully punished malicious actions by confiscating their deposits and requiring them to pay up to four times of base deposit value when they join the next update task, ensuring the fairness of our protocol. Furthermore, we generate low processing delay overhead compared to existing works that rely on Zero-Knowledge Proofs. The gas usage consumption from our approach also produced a competitive result despite our works supporting more features than existing works, ensuring the efficiency of our proposal.
Secure aggregation enables federated learning (FL) to perform collaborative training of clients from local gradient updates without exposing raw data. However, existing secure aggregation schemes inevitably perform an expensive fresh setup per round because each client needs to establish fresh input-independent secrets over different rounds. The latest research, Flamingo (S&P 2023), designed a share-transfer-based reusable secret key to support the server continuously performing multiple rounds of aggregation. Nevertheless, the share transfer mechanism it proposed can only be achieved with P probability, which has limited reliability. To tackle the aforementioned problems, we propose a more reliable and anonymously authenticated scheme called Chu-ko-nu for multi-round secure aggregation. Specifically, in terms of share transfer, Chu-ko-nu breaks the probability P barrier by supplementing a redistribution process of secret key components (the sum of all components is the secret key), thus ensuring the reusability of the secret key. Based on this reusable secret key, Chu-ko-nu can efficiently perform consecutive aggregation in the following rounds. Furthermore, considering the client identity authentication and privacy protection issue most approaches ignore, Chu-ko-nu introduces a zero-knowledge proof-based authentication mechanism. It can support clients anonymously participating in FL training and enables the server to authenticate clients effectively in the presence of various attacks. Rigorous security proofs and extensive experiments demonstrated that Chu-ko-nu can provide reliable and anonymously authenticated aggregation for FL with low aggregation costs, at least a 21.02% reduction compared to the state-of-the-art schemes.
The rapid evolution of intelligent systems has led to the emergence of distributed artificial intelligence architectures where multiple agents collaborate to solve complex problems. However, enabling secure and trustworthy collaboration among distributed agents remains a major challenge due to issues related to data integrity, privacy, and trust management. Blockchain technology offers a decentralized and tamper-resistant infrastructure that can address these challenges by enabling transparent and secure coordination among intelligent agents. This paper proposes a blockchain-enabled distributed intelligence platform designed to support secure multi-agent collaboration across decentralized environments. The architecture integrates smart contracts, distributed ledgers, and machine learning agents to create a trusted computational ecosystem where agents can exchange data, validate actions, and coordinate decisions without relying on centralized authorities.
Jonathan Ku, Junyao Zhang, Haoxuan Shan, Saichand Samudrala · 9 authors
Elliptic curve cryptography (ECC) is widely used in security applications such as public key cryptography (PKC) and zero-knowledge proofs (ZKP). ECC is composed of modular arithmetic, where modular multiplication takes most of the processing time. Computational complexity and memory constraints of ECC limit the performance. Therefore, hardware acceleration on ECC is an active field of research. Processing-in-memory (PIM) is a promising approach to tackle this problem. In this work, we design ModSRAM, the first 8T SRAM PIM architecture to compute large-number modular multiplication efficiently. In addition, we propose R4CSA-LUT, a new algorithm that reduces the cycles for an interleaved algorithm and eliminates carry propagation for addition based on look-up tables (LUT). ModSRAM is co-designed with R4CSA-LUT to support modular multiplication and data reuse in memory with 52% cycle reduction compared to prior works with only 32% area overhead.
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.
Abstract With the rapid development of the Internet of Medical Things (IoMT) and the increasing concern for personal health, sharing Electronic Medical Record (EMR) data is widely recognized as a crucial method for enhancing the quality of care and reducing healthcare expenses. EMRs are often shared to ensure accurate diagnosis, predict prognosis, and provide health advice. However, the process of sharing EMRs always raises significant concerns about potential security issues and breaches of privacy. Previous research has demonstrated that centralized cloud-based EMR systems are at high risk, e.g., single points of failure, denial of service (DoS) attacks, and insider attacks. With this motivation, we propose an EMR sharing scheme based on a consortium blockchain that is designed to prioritize both security and privacy. The interplanetary file system (IPFS) is used to store the encrypted EMR while the returned hash addresses are recorded on the blockchain. Then, the user can authorize other users to decrypt the EMR ciphertext via the proxy re-encryption algorithm, ensuring that only authorized personnel may access the files. Moreover, the scheme attains personalized access control and guarantees privacy protection by employing attribute-based access control. The safety analysis shows that the designed scheme meets the expected design goals. Security analysis and performance evaluation show that the scheme outperforms the comparison schemes in terms of computation and communication costs.