Under the rapid development of big data and cloud computing, emerging applications have seen significant improvements in efficiency and service quality. Nevertheless, the conflict between data sharing and privacy preservation remains a major obstacle to the advancement of big data technology. Addressing this issue, this study introduces a solution tailored to the big data environment, which achieves privacy protection and auditability in data sharing and processing. This approach separates data ownership, usage, and validation to mitigate privacy breaches and improper computing behaviors. Leveraging blockchain technology, a transparent governance platform is constructed to identify and track illegal data and computing activities. Furthermore, the solution integrates noninteractive zero-knowledge proofs for publicly verifying data consistency and computing validity on the blockchain. Experimental analysis on computational latency, communication costs, and encryption parameters confirms the feasibility and efficacy of this approach.
In distributed computing, data trading mechanisms are essential for ensuring the sharing of data across multiple computing nodes. Nevertheless, they currently encounter considerable obstacles, including low accuracy in matching trading parties, ensuring fairness in transactions, and safeguarding data privacy throughout the trading process. In order to address these issues, we put forward a data trading security scheme based on zero-knowledge proofs and smart contracts. In the phase of preparing the security parameters, the objective is to reduce the complexity of generating non-interactive zero-knowledge proofs and to enhance the efficiency of data trading. In the pre-trading phase, we devise attribute atomic matching smart contracts based on precise data property alignment, with the objective of achieving fine-grained matching of data attributes between trading parties. In the trading execution phase, lightweight cryptographic algorithms based on elliptic curve cryptography (ECC) and non-interactive zero-knowledge proofs are employed for the dual encryption of trading data and the generation of attribute proof contracts, thus ensuring the security and privacy of the data. The results of experiments conducted on the Ethereum platform in an industrial IoT scenario demonstrate that our scheme maintains stable and low-cost consumption while ensuring accuracy in matching and privacy protection.
M. Okada, Ahmad Akmal Aminuddin Mohd Kamal, Masaya Fujisawa
Blockchains (BCs) have garnered attention owing to their potential applications in a wide range of fields, including finance and the Internet of Things. Nakajima et al. introduced a secure and decentralized storage system using a permissioned BC and secret sharing. However, this method employs conventional password-based user authentication with a single SDK server. In this study, we introduce a secure and decentralized user authentication system that employs secure computation based on Shamir’s (2,2) threshold secret sharing and smart contracts. We demonstrate that employing secure computation using secret sharing facilitates user authentication while maintaining the confidentiality of the original password. Furthermore, we show that even if the information registered on the SDK server is known, an attacker will not be able to recover the original password.
The detection of vulnerabilities in smart contracts remains a significant challenge. While numerous tools are available for analyzing smart contracts in source code, only about 1.79% of smart contracts on Ethereum are open-source. For existing tools that target bytecodes, most of them only consider the semantic logic context and disregard function interface information in the bytecodes. In this paper, we propose COBRA, a novel framework that integrates semantic context and function interfaces to detect vulnerabilities in bytecodes of the smart contract. To our best knowledge, COBRA is the first framework that combines these two features. Moreover, to infer the function signatures that are not present in signature databases, we propose SRIF, automatically learn the rules of function signatures from the smart contract bytecodes. The bytecodes associated with the function signatures are collected by constructing a control flow graph (CFG) for the SRIF training. We optimize the semantic context using the operation code in the static single assignment (SSA) format. Finally, we integrate the context and function interface representations in the latent space as the contract feature embedding. The contract features in the hidden space are decoded for vulnerability classifications with a decoder and attention module. Experimental results demonstrate that SRIF can achieve 94.76% F1-score for function signature inference. Furthermore, when the ground truth ABI exists, COBRA achieves 93.45% F1-score for vulnerability classification. In the absence of ABI, the inferred function feature fills the encoder, and the system accomplishes an 89.46% recall rate.
Data privacy preservation and secure sharing are key technical challenges faced by smart wearable healthcare Internet of Things (IoT) systems. Blockchain technology enables privacy preservation for medical data through encryption. However, conventional data encryption hampers data analysis and sharing, and decrypted data still carries the risk of leakage. Homomorphic encryption is a technique that allows computation directly on encrypted data without decryption, thus reducing the risk of data leakage during sharing. In this article, we propose a blockchain-based privacy preservation and sharing scheme for healthcare IoT data. First, we use an improved homomorphic encryption technique to encrypt and process electronic health records (EHRs), optimizing the modular exponentiation process with a fast exponentiation algorithm, enabling users to efficiently perform data computation and analysis while keeping the data encrypted. Second, we employ symmetric searchable encryption (SSE) to encrypt homomorphic keys and user identity information, and use a Bloom filter as the mapping structure between data keywords and unique identifiers. This approach enhances search efficiency while preserving data privacy, allowing for secure search and analysis on ciphertext. Finally, smart contracts are designed to implement access control during the data-sharing process, increasing the security and transparency of data sharing. Experimental results show that the proposed homomorphic encryption scheme reduces the encryption and decryption time by an average of 34% under different key sizes, while the optimized SSE technique keeps ciphertext retrieval time at a constant level. The proposed scheme provides an effective solution for secure and efficient data analysis and retrieval, ensuring privacy preservation for the secure use and sharing of medical data.
Philip Huff, Spencer Massengale, Tran Viet Xuan Phuong, Sri Nikhil Gupta Gourisetti
Cyber Threat Intelligence (CTI) is a key resource for developing defensive strategies against potential cyber adversaries. Entities typically access CTI through open-source platforms, national agencies, or specialized commercial services. However, the bi-directional exchange of CTI is hindered by organizational trust boundaries, which complicate the sharing processes between entities and CTI providers. Centralized CTI services benefit from receiving suspicious cyber observables such as IP addresses, domain names, and email addresses from various entities. The aggregation allows for the correlation of widespread adversarial activities to enhance the alert and response mechanisms across the network of involved parties. Despite these benefits, openly sharing such observables incurs potential legal, regulatory, and reputational risks for the disclosing entities.This paper introduces a system designed to facilitate the secure exchange of cyber observables across trust boundaries without compromising the anonymity of the sharing entities. We propose an architecture that leverages common web protocols alongside zero-knowledge proofs to authenticate members while maintaining anonymity. Additionally, we outline a privacy model tailored for STIX (Structured Threat Information eXpression) cyber observables to minimize the risk of inadvertently disclosing private information. Through our threat models, we assess the privacy implications of our proposed system and demonstrate its potential to enhance collaborative cyber defense efforts without exposing entities to undue risk.
As the Internet of Things (IoT) continues to evolve, the ability to share data has become an integral aspect of cloud computing services.Nevertheless, the persistent issue of data security presents a formidable challenge within the domain.This study introduces a novel blockchain oriented data sharing architecture designed to reinforce data security while optimizing efficiency.The architecture is structured around advanced smart contracts and security mechanisms that register cloud-based data activities on a blockchain ledger.In instances of anomalous activities, the blockchain is scrutinized by a centralized cloud service to identify and hold accountable any malicious gateways.The framework employs robust authentication and secure data transmission protocols to fortify data security.Furthermore, it utilizes sophisticated yet efficient partial decryption algorithms within smart contracts to alleviate the computational load on end users.Blockchain's capability to provide traceable historical records underpins the system's ability to meet stringent data safety standards through transparent and open oversight.Empirical evidence underscores the effectiveness of the proposed system in safeguarding data exchanges across various clients while maintaining high operational efficiency.
Access control in large systems poses challenges in ensuring secure and flexible management of user privileges. Current approaches often rely on centralized servers, leading to concerns about data privacy and reliability. This study we study the benefits and overheads of integrating Blockchain technology and embedded devices for access control in large systems. We propose a Blockchain-based Non-Fungible Token (NFT) mechanism for access control of private data, leveraging the distributed and immutable nature of Blockchain to enhance security and scalability. This approach is versatile, applying to various access control scenarios. For instance, in Person-Device, where individuals are granted access to specific devices; Person-Data-Device, where individuals are given access to specific data on specific devices; and Person-Time-Device, where access is granted to individuals for specific periods to specific devices.In our study, we evaluated this approach using embedded devices such as ESP32 microcontrollers, Beaglebone Black boards, and Raspberry Pi. Our results show that a blockchain-based approach is suitable for resource-constrained devices, requiring minimal computational resources and introducing negligible delays. We find that our approach can be used to facilitate secure access to private data, ensuring confidentiality and integrity in a distributed setup without requiring any centralized control.
Decentralized AI promises to unlock the potential of fragmented data across domains like healthcare and finance, but faces significant challenges in trust and incentive alignment. This paper proposes a comprehensive framework addressing these challenges through advanced privacy-preserving techniques and federated data markets. We integrate collaborative inference, split learning, and synthetic data sharing to establish trust, while leveraging federated data valuation and acquisition to foster fair and efficient data markets. Our systematic review identifies areas of progress in decentralized AI and highlights key research opportunities. This analysis focuses on overcoming barriers to collaborative AI development while preserving data sovereignty, paving the way for more inclusive and innovative AI ecosystems.
In the era of digital transformation, securing data on metaverse platforms poses significant challenges. This paper proposes Multiparty Space Sharing and Authentication (MSSA), a novel approach for secure user login and location access control within specialized metaverse platforms. MSSA leverages Quantum Multiparty Secret Computation (QMSC) integrated with a quantum blockchain network. This integration facilitates user verification within the presence of potentially untrusted metaverse authority. The underlying quantum blockchain employs a Delegated Proof-of-Stake (DPoS) consensus mechanism with a Borda voting scheme for authority node selection. By harnessing the principles of quantum cryptography, MSSA offers enhanced security against both classical and anticipated future quantum attacks. This research demonstrates the feasibility and potential of quantum blockchain for securing metaverse platforms, paving the way for secure and decentralized digital ecosystems.
The Internet of Medical Things (IoMT) is revolutionising the healthcare landscape by seamlessly integrating medical devices, sensors, and healthcare information systems. This interconnected network of devices is designed to improve patient outcomes, enhance healthcare delivery, and streamline medical processes. However, as IoMT continues to evolve, it introduces new challenges related to data security, privacy, and interoperability. Blockchain technology has emerged as a promising solution to address these challenges, offering a decentralised and secure framework for managing health-related data in IoMT applications. This research aims to implement a blockchain-enabled network within a Federated Learning-based Internet of Medical Things (IoMT) environment. The proposed framework features a centralized server hosting a global machine learning model. IoMT devices operate with local models that run concurrently with the global model, incorporating device-specific data. Simulations and comparisons have been conducted on the predominant consensus models, namely Proof of Stake and Proof of Work. These ongoing initiatives aspire to play a role in enhancing the security and privacy aspects of the latest developments in the Internet of Medical Things.
The diversity and scarcity of the medical information makes it difficult to create precise global classification approach for the healthcare applications.The main motive is the privacy issue that restricts the data exchanging scope between healthcare institutions.On the contrary, an information from single source is not adequate for developing the worldwide diagnosis approach.The Federated Learning (FL) is a promising solution for privacy and data multiplicity issues, an appropriate aggregation model for multi class and dissimilar medical information is still challenging task in the recognition.Moreover, the FL approaches does not effectively analyzes the each participant execution in the local model and secures the user data.In order to overcome this issue, the Zero-Knowledge Proof (ZKP) based FL approach is developed over blockchain (BC) for performing the COVID-19 classification.The global model of FL uses the two layer Long Short Term Memory (2LLSTM) with federated proximal term (FedProx) namely 2LLSTMFP while the Convolutional Neural Network (CNN) is used in the local model.The integration ZKP and BS is used to improve the data confidentiality while the immutability of BC helps to prevent unauthorized variations for the ledger.The developed FLBC-ZKP is analyzed with two datasets such as COVID-19 Radiography, and CXR images pneumonia and COVID-19.The FLBC-ZKP is evaluated using accuracy, recall, precision, specificity, F1-score, False Negative Rate (FNR) and False Positive Rate (FPR).The existing researches such as WMT, MCCF, 3SFDL and TOTL are used to compare the FLBC-ZKP method.The FLBC-ZKP achieves improved accuracy of 98.34 % for COVID-19 Radiography dataset that is better than the MCCF and 3SFDL.
This paper presents a peer-to-peer electricity trading framework with dynamic pricing designed to enhance energy availability and economic returns for market participants. A key feature of the proposed model is the use of federated learning for predictive pricing for energy, ensuring data privacy while considering user preferences. Simulation results validate that this approach significantly improves energy availability and financial benefits. The method is implemented on the Ethereum public blockchain, providing a secure and transparent trading environment. Real-world data tests further confirm the efficacy of the system, demonstrating enhanced reliability and economic gains for participants in decentralized energy markets.
Chi Xu, Peifeng Zhang, Xiaofang Xia, Linghe Kong · 6 authors
Blockchain-based vehicular edge computing (VEC) is regarded as a promising computing paradigm that can enhance the computing capabilities of mobile vehicles while ensuring security during task offloading. However, the blockchain consensus for secure task offloading inevitably increases the communication and computation resource consumption. More importantly, the frequent handover among roadside units during the fast movement of vehicles also raises the communication cost for blockchain consensus. To address these issues, this article proposes intelligent secure task offloading and caching (ISTOC) scheme for VEC networks. Specifically, we first establish a digital twin-assisted VEC network that migrates the blockchain consensus process from the physical space to the cyber space, supporting the dynamic handover of vehicles. Correspondingly, we propose a lightweight blockchain scheme named diffused delegated Byzantine fault tolerance (d2BFT). Then, aiming at simultaneously reducing the task processing latency and improving the blockchain transaction throughput, we formulate the joint blockchain, communication, computation, and caching (B3C) optimization problem subject to task division, communication bandwidth, computing frequency, cache storage, task deadline, and blockchain stability. Due to the nonconvexity of B3C, we transform it into a Markov decision process, and propose a multiagent double actor-critic (MADAC) algorithm in light of the distributed characteristic of blockchain. Through offline training and online execution, we jointly optimize the task division, communication bandwidth, computing frequency and cache storage allocation, block size, and block generation interval for ISTOC. Experimental results show that the proposed MADAC-based ISTOC scheme can stably converge with a much higher reward than the benchmark schemes based on MADDPG, soft actor-critic, deep deterministic policy gradient, and TD3. The improvement of MADAC-ISTOC over SAC-ISTOC is more than 25.93%.
In this study, a blockchain-based Decentralized Application (DApp) for certificate authentication and verification from accredited colleges or certification bodies is presented. The DApp uses decentralized ledger technology, built on the Ethereum blockchain and smart contracts, to guarantee the integrity and authenticity of digital certificates. It uses a number of technologies, including Streamlit, Ganache-cli, Truffle, Pinata (for IPFS), and Docker, to expedite the certificate validation process and lower the risk of manipulation and forgery. The Institute, which is in charge of awarding certifications, and the Verifier, which is in charge of providing strong verification procedures, are the two primary players in the system. By utilizing Pinata, IPFS improves certificate permanence and accessibility without depending on centralized data storage. The advantages of blockchain attributes such as immutability, transparency, and decentralization in building confidence in the certification and education domains are empirically demonstrated. Future studies will concentrate on compatibility with other blockchain ecosystems, scalability, and smart contract efficiency. All things considered, this work provides a framework for safe document verification and recommends blockchain-based DApps as a remedy for data security and integrity in a variety of industries.
Blockchain technology has gained significant attention for its ability to provide a decentralized and immutable platform for various applications. In this paper, we propose a Blockchain based Decentralized Case Management System using the InterPlanetary File System (IPFS) protocol. The decentralized nature of blockchain ensures data integrity and transparency, while IPFS facilitates efficient and decentralized storage of case files. Our system allows for secure and fine-grained access control to case data, ensuring that only authorized users can view and manage cases. We present the architecture of the system, which includes smart contracts deployed on the blockchain to manage case data and access control rules. Additionally, we have discussed the implementation details and use case scenarios to demonstrate the functionality of the proposed system. Performance testing revealed that our system significantly enhances data integrity and transparency. Comparative analysis demonstrated that IPFS, despite having slightly higher latencies than traditional FTP, provides superior security and redundancy. Through our research, we aim to provide a secure, transparent, and efficient solution for managing cases in various domains, including legal, healthcare, and finance.
This article explores the transformative potential of blockchain technology in revolutionizing data management and enhancing transparency across various industries. As a decentralized, immutable ledger system, blockchain offers a secure and efficient way to record, share, and verify information, eliminating the need for intermediaries and reducing the risk of fraud. The article reviews the fundamental principles of blockchain, including distributed consensus, cryptographic security, and smart contracts, to highlight how these features improve data integrity and trust in digital transactions. Through case studies in sectors such as finance, supply chain management, healthcare, and government services, the article demonstrates how blockchain can streamline operations, reduce costs, and promote accountability. Particular attention is given to the role of blockchain in improving transparency by providing an auditable and tamper-proof record of transactions, which is especially valuable in industries prone to corruption and data manipulation. The article also addresses the challenges facing blockchain adoption, including scalability issues, regulatory concerns, and the need for standardization. Additionally, the potential of blockchain in emerging applications, such as decentralized finance (DeFi) and non-fungible tokens (NFTs), is discussed, highlighting the technology’s expanding role in reshaping digital economies. The article concludes by emphasizing that while blockchain is still in its developmental stages, its ability to foster greater transparency and more secure data management systems positions it as a key driver of innovation in the digital age. Future research is recommended to explore further integration strategies and address challenges in widespread blockchain adoption.
This paper addresses privacy protection in decentralized Artificial Intelligence (AI) using Confidential Computing (CC) within the Atoma Network, a decentralized AI platform designed for the Web3 domain. Decentralized AI distributes AI services among multiple entities without centralized oversight, fostering transparency and robustness. However, this structure introduces significant privacy challenges, as sensitive assets such as proprietary models and personal data may be exposed to untrusted participants. Cryptography-based privacy protection techniques such as zero-knowledge machine learning (zkML) suffers prohibitive computational overhead. To address the limitation, we propose leveraging Confidential Computing (CC). Confidential Computing leverages hardware-based Trusted Execution Environments (TEEs) to provide isolation for processing sensitive data, ensuring that both model parameters and user data remain secure, even in decentralized, potentially untrusted environments. While TEEs face a few limitations, we believe they can bridge the privacy gap in decentralized AI. We explore how we can integrate TEEs into Atoma's decentralized framework.
Blockchain-assisted federated learning (BFL) can achieve decentralized storage and management of model data without relying on a central server. However, security issues caused by deliberate attacks in distributed systems and efficiency issues induced by heterogeneous computing consumption in resource-limited systems need to be urgently addressed in BFL. To address these issues, we propose a decentralized reputation management (DRM) mechanism for a trustworthy BFL (T-BFL) network, that explores, stores, and utilizes the endogenous reputation of distributed nodes to promote system security and efficiency. The proposed DRM includes three core modules, i.e., decentralized reputation evaluation, reputation-based model aggregation, and reputation-based blockchain consensus. Specifically, in the off-chain phase of T-BFL, the reputation value of each node is evaluated based on model quality, which other peer nodes can verify. This reputation value further determines the weight of global aggregation at each node. In the on-chain phase, the reputation of each node serves as the stake to dynamically adjust its consensus difficulty. Furthermore, we investigate the convergence rate of the T-BFL network under the poisoning attack, and dynamically optimize the energy allocation of local training, consensus, and communications by minimizing the upper bound of the global loss function. Extensive experiments are conducted to evaluate the performance of T-BFL on MNIST, Fashion-MNIST, and Cifar-10 datasets. The experimental results demonstrate that, compared with traditional BFL, T-BFL can achieve up to 56.12% accuracy improvement and$8.6\times $acceleration for reaching the target learning accuracy under the poisoning attack.