A Big Data environment is a robust ecosystem in Healthcare data analysis to extract sensitive information from Medical Personal Healthcare Records (MPHR) to preserve privacy to protect sensitive information. From the data analysis context, the sensitive and non-sensitive information is identical in behavioral approach, so those sensitive items have a similar frequency of access in all security roles, leading to privacy and security especially becoming crucial issues. Thus, the existing problems are the most non-sensitive approach to dealing with the privacy standard in the form of low performance and lead time complexity. Information regarding exposure and risk-response relationships is crucial for estimating the burden of illness caused by environmental variables. The world's efforts to promote sound preventative measures through existing policies, methods, measures, methods, and knowledge can be bolstered by a better grasp of the extent to which disease and ill health is attributable to adjustable risks related to the environment. To tackle these issues, we propose an MPHR- Sensitive data prediction system based on a Pragmatic attribute Identifier Using Advance Blockchain security to Secure the Sensitive data in the big data healthcare environment. Initially, the proposed technique pre-processes the PHR information to eliminate potential errors. Then Sensitive Scaling Impact Rate (SSIR) method is used to identify the sensitive and non-sensitive marginal values. Based on the marginal values, the proposed Pragmatic Sensitive Feature Clustering Algorithm (PSFCA) is used to analyze the importance of sensitive feature relations. Next, the sensitive relation is fed into the Densenet Convolutional Neural Network (Densnet-CNN) method to classify the sensitive and non-sensitive attributes. Further, the security principle is applied based on Smart Contract Master Aggregation (SCMA) based blockchain health care security. The proposed approach outperforms existing privacy preservation in sensitive data prediction systems in terms of blockchain privacy and security, according to the findings of the experiments.
Aitizaz Ali, Bander Ali Saleh Al‐rimy, Faisal S. Alsubaei, Abdulwahab Ali Almazroi · 5 authors
The swift advancement of the Internet of Things (IoT), coupled with the growing application of healthcare software in this area, has given rise to significant worries about the protection and confidentiality of critical health data. To address these challenges, blockchain technology has emerged as a promising solution, providing decentralized and immutable data storage and transparent transaction records. However, traditional blockchain systems still face limitations in terms of preserving data privacy. This paper proposes a novel approach to enhancing privacy preservation in IoT-based healthcare applications using homomorphic encryption techniques combined with blockchain technology. Homomorphic encryption facilitates the performance of calculations on encrypted data without requiring decryption, thus safeguarding the data's privacy throughout the computational process. The encrypted data can be processed and analyzed by authorized parties without revealing the actual contents, thereby protecting patient privacy. Furthermore, our approach incorporates smart contracts within the blockchain network to enforce access control and to define data-sharing policies. These smart contracts provide fine-grained permission settings, which ensure that only authorized entities can access and utilize the encrypted data. These settings protect the data from being viewed by unauthorized parties. In addition, our system generates an audit record of all data transactions, which improves both accountability and transparency. We have provided a comparative evaluation with the standard models, taking into account factors such as communication expense, transaction volume, and security. The findings of our experiments suggest that our strategy protects the confidentiality of the data while at the same time enabling effective data processing and analysis. In conclusion, the combination of homomorphic encryption and blockchain technology presents a solution that is both resilient and protective of users' privacy for healthcare applications integrated with IoT. This strategy offers a safe and open setting for the management and exchange of sensitive patient medical data, while simultaneously preserving the confidentiality of the patients involved.
Decentralized identity frameworks grant users full sovereignty over their digital assets in the Web3 ecosystem. However, allowing arbitrary creation of identifiers makes the system susceptible to Sybil attacks and puts assets at risk when keys are lost or compromised. Moreover, the lack of identification prevents anonymous credential schemes from deterring malicious transfers. While existing solutions attempt to address these issues by linking identifiers to entities through trusted intermediaries, these entities are not always accessible and require costly offline interactions. In this work, we introduce LinkDID, a decentralized identity scheme offering Sybil resistance, trustless key recovery, and nontransferable anonymous credentials. LinkDID creates blockchainbased bindings between identifiers and gradually combines identifiers belonging to the same holder into a unified associated identifier. As all identifiers within an association are presumed to belong to one individual, any fraudulent activity can be detected. The association grows larger as interactions increase, substantially reducing the likelihood of successful Sybil attacks. This mechanism allows holders to recover identifiers with lost or stolen keys by proving knowledge of specific association structures. Additionally, LinkDID prevents unauthorized transfers through blockchain-based identifier-key bindings and proofs of ownership for credentials. The evaluation shows that LinkDID effectively achieves progressive Sybil resistance while surpassing state-of-the-art anonymous credential schemes, achieving identifier association and credential presentation times of 2.41s and 3.31s on consumer-grade devices.
Antonio Yaghy, Nicole Rose I. Alberto, Isabelle Rose I. Alberto, Rene S. Bermea · 9 authors
Non-fungible tokens (NFTs) are cryptographic assets recorded on the blockchain that can certify authenticity and ownership, and they can be used to monetize health data, optimize the process of receiving a hematopoietic stem cell transplant, and improve the distribution of solid organs for transplantation. Blockchain technology, including NFTs, provides equitable access to wealth, increases transparency, eliminates personal or institutional biases of intermediaries, reduces inefficiencies, and ensures accountability. Blockchain architecture is ideal for ensuring security and privacy while granting individuals jurisdiction over their own information, making it a unique solution to the current limitations of existing health information systems. NFTs can be used to give patients the option to monetize their health data and provide valuable data to researchers. Wearable technology companies can also give their customers the option to monetize their data while providing data necessary to improve their products. Additionally, the process of receiving a hematopoietic stem cell transplant and the distribution of solid organs for transplantation could benefit from the integration of NFTs into the allocation process. However, there are limitations to the technology, including high energy consumption and the need for regulatory guidance. Further research is necessary to fully understand the potential of NFTs in healthcare and how it can be integrated with existing health information technology. Overall, NFTs have the potential to revolutionize the healthcare sector, providing benefits such as improved access to health information and increased efficiency in the distribution of organs for transplantation.
The Internet of Medical Things (IoMT) builds a bridge between patients and doctors, facilitating patients’ being diagnosed and monitored by uploading physiological indicators without visiting the hospital. However, physiological indicators are sensitive data of patients, making it a challenge to achieve verifiability of data sources while ensuring data privacy during data transmission of IoMT. Due to its ease of deployment and the ability to provide both encryption and signature, certificateless signcryption (CLSC) is suitable for designing secure data-transfer protocol in IoMT. Nevertheless, internal adversaries “malicious users” and “malicious KGC,” capable of launching Type I and Type II attacks, threaten the security of present CLSC schemes, making most of them insecure. In this work, after giving an example of a recent CLCS scheme suffering Type I attack, we propose an efficient pairing-free CLCS scheme suitable for secure data transmission in IoMT based on the idea of zero-knowledge proof. It not only provides confidentiality and unforgeability of transmitted data under the Type I and Type II attacks but also achieves lower computational and communication overhead, and public verifiability. Finally, compared with the five recent CLSC schemes, theoretical analysis and experimental testing results show that the proposed scheme outperforms the other five schemes in terms of computation and communication costs as well as security. Therefore, our scheme is better suited for constructing secure data transmission in IoMT scenarios.
As the Industrial Internet of Things (IIoT) continues to grow in scale, edge devices will generate massive amounts of data every single day. However, most of the IIoT data exists in the form of data silos, which makes it difficult to share data across domains securely. Therefore, a secured data-sharing scheme for IIoT based on blockchain and federated learning (FL) is proposed in this article. Leveraging blockchain in FL systems to enhance the tamper-proof and decentralized capabilities of IIoT devices. Model parameter validation and incentives are also added to the consensus algorithm to encourage more IIoT data owners to contribute local privacy data and arithmetic power. To address potential security issues, such as parameter leakage and inference attacks in data sharing, this article designs an adaptive differential privacy mechanism and a node contribution consensus mechanism. Without affecting the global model’s accuracy, some of the noise is also reduced. The reputation mechanism is used to resist poisoning attacks by malicious nodes. It is demonstrated on different data sets that our scheme has high global model accuracy and can effectively resist 30% model poisoning attacks.
Managing and exchanging sensitive information securely is a paramount concern for the scientific and cybersecurity community. The increasing reliance on computing workflows and digital data transactions requires ensuring that sensitive information is protected from unauthorized access, tampering, or misuse. This research paper presents a comparative analysis of three novel approaches for authenticating and securing access to scientific data: SciTokens, Verifiable Credentials, and Smart Contracts. The aim of this study is to investigate the strengths and weaknesses of each approach from trust, revocation, privacy, and security perspectives. We examine the technical features and privacy and security mechanisms of each technology and provide a comparative synthesis with the proposed model. Through our analysis, we demonstrate that each technology offers unique advantages and limitations, and the integration of these technologies can lead to more secure and efficient solutions for authentication and access to scientific data.
Due to the increasing risk of data security, distributed learning model based on real-world data analytics has attracted more attention, and it has been applied in a variety of areas ranging from medical screening to agriculture, industry, finance, and defense science. Generally, participants provide their own private datasets to efficiently train the distributed models on real-world data, which inevitably leads to privacy and security concerns. Without uploading raw training data, Federated Learning enables large-amount nodes to train a distributed model and preserves security and privacy of user sensitive information. However, Federated Learning is limited by expensive computational costs during collective parameter server aggregation. Moreover, malicious nodes among computing nodes interfere model training to some extent and further cause the leakage of data privacy. To address the above-mentioned problems, we propose a novel Decentralized Federated Learning by integrating blockchain and federation learning for efficient node selection and communication. Based on the proposed model, a reputation-based learning nodes selection algorithm is presented to measure the probability of honest participation of distributed nodes. The simulation results demonstrate that our RBLNS is capable of improving the training result significantly and decreasing convergence time.
Zero-knowledge proof is emerging to enable privacy. Among existing techniques, zk-SNARK (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) [1] supports the shortest verification time and the smallest proof size. However, using zk-SNARK requires the execution of trusted setup ceremony in advance. The trusted setup ceremony generates common reference string (CRS) which is shared with prover and verifier. Currently, an external trusted third party is assumed for trusted setup ceremony, which causes significant security vulnerability in zk-SNARK. In this paper, we propose a blockchain-based protocol of trusted setup ceremony without trusted third party. Three different types of protocols are classified in terms of where to store CRS and how to validate CRS through pairing check. We analyze the protocol complexity of CRS pairing check computations and on-chain storage space.
Multi-brain Federated Learning (MBFL) introduces an innovative approach to decentralized artificial intelligence, enabling joint model training across various fields while maintaining data privacy. This study clarifies the MBFL concept and explores its potential uses in industries such as healthcare, finance, and defense. It covers the core principles of MBFL such as data decentralization, model aggregation, and privacy-preserving techniques. The benefits of MBFL, including improved model performance and reduction of data silos, are examined along with possible challenges and limitations. A framework for implementing MBFL in different scenarios was provided, and its impact on the future direction of AI development was discussed. The paper concludes by highlighting the transformative potential of MBFL in advancing collaborative AI, while ensuring data security and privacy. Keywords — Multi-brain Federated Learning, Decentralized AI, Privacy-preserving, Collaborative models, Data security, Cross- domain learning, Model aggregation, Federated Learning, Healthcare, Finance, Defense.
Training and deploying the large language models requires a large mount of computational resource because the language models contain billions of parameters and the text has thousands of tokens. Another problem is that the large language models are static. They are fixed after the training process. To tackle these issues, in this paper, we propose to train and deploy the dynamic large language model on blockchains, which have high computation performance and are distributed across a network of computers. A blockchain is a secure, decentralized, and transparent system that allows for the creation of a tamper-proof ledger for transactions without the need for intermediaries. The dynamic large language models can continuously learn from the user input after the training process. Our method provides a new way to develop the large language models and also sheds a light on the next generation artificial intelligence systems.
Tahiry Rabehaja, Shantanu Pal, Ambrose Hill, Michael Hitchens
The use of parametric insurance is promising as its payouts can be directly tied to hazard indicators and thus provide a fast-tracked claim-to-payout process, which improves liquidity in times of disaster. In parametric insurance, policies are determined by a loss threshold (modelled or sustained) or physical hazard severity (e.g., rainfall or wind speed). In the latter, when the severity of the hazard exceeds a threshold, a payout is automatically triggered according to the insurance contract terms to compensate the policyholder without needing a loss assessment. Recently, blockchains have been proposed to improve the efficiency of insurance product offerings (e.g., to cut administrative costs associated with the premium collection and claim processing) and to efficiently store and maintain information (e.g., immutable distributed storage for audits). While parametric insurance would certainly benefit from these blockchain implementations, existing proposals mostly depend on a single source of truth for payout calculations. In this paper, we present a novel trust-based framework to handle multiple sources of truth in parametric insurance products which use blockchain technology. This framework alleviates the reliance on a single point of failure (either through accidents or malicious abuses) and outperforms its statistical counterparts. We discuss how parametric insurance would work under such a framework using real-world use-case scenarios, show the use of subjective logic to reason about multiple sources of truth, present the architecture of the framework, and examine a detailed blockchain-based implementation using an Ethereum private blockchain. Our results show the feasibility of the proposed system in practice.
Due to the significance of trust in Social Internet of Things (SIoT)-based smart marketplaces, several research have focused on trust-related challenges. Trust is necessary for a smooth connection, secure systems, and dependable services during trade operations. Recent SIoT-based trust assessment approaches attempt to solve smart marketplace trust evaluation difficulties by using a variety of direct and indirect trust evaluation techniques and other local trust rating procedures. Nevertheless, these methodologies render trust assessment very sensitive to seller dishonesty, and a dishonest seller may influence local trust scores and at the same time pose a significant trust related threats in the system. In this article, a MarketTrust model is introduced, which is a blockchain-based method for assessing trust in an IoT-based smart marketplace. It has three parts: familiarity, personal interactions, and public perception. A conceptual model, assessment technique, and a global trust evaluation system for merging the three components of a trust value are presented and discussed. Several experiments were conducted to assess the model's security, viability, and efficacy. According to results, the MarketTrust model scored a 21.99% higher trust score and a 47.698% lower average latency than both benchmark models. Therefore, this illustrates that using the proposed framework, a potential buyer can efficiently choose a competent and trustworthy resource seller in a smart marketplace and significantly reduce malicious behavior.
Spectrum distribution is a classical licensed spectrum accessing method in mobile communication networks. The licensed idle spectrum resources are authorized and distributed from spectrum owners to mobile users. However, the exponential growth of user capacity brings excessive load pressure on the traditional centralized network architecture. With a lack of sufficient supervision and penalty measures, dishonest behaviors of spectrum owners and spectrum users will lead to an unfair status in the distribution process. As a result, the honest participants’ interest will be harmed. As an important supporting infrastructure of Internet of Things technology, 6G cannot completely follow the existing spectrum distribution method. Towards 6G network spectrum distribution, a blockchain-based licensed spectrum fair distribution method is proposed. A lightweight consensus mechanism named proof of trust (PoT) is applied to reduce computational power consumption and consensus time overhead. We deploy the method on the Ethereum test chain; a theoretical analysis and experimental results demonstrate the fairness, effectiveness and security of the method.
A well-known use of the blockchain technology is Decentralized Finance (DeFi). DeFi makes financial information accessible to the public but raises potential privacy and security issues. In this study, we implemented a DeFi protocol that protects privacy, which is based on the Mystiko.Network protocol. As a proxy between the user and DeFi platforms, the Mystiko.Network protocol offers an auditable confidentiality mechanism for blockchain transactions. Via the new system, users may submit anonymous DeFi transactions and get income back into a shielded tokens pool. Moreover, we implemented a rollup approach to handle anonymous DeFi transactions in groups. The evaluation results suggest that the protocol is both practical and affordable, in fact it is able to save around 90% of the cost for DeFi transactions.
With the rapid growth of flight operation data, how to alleviate the contradiction between data sharing and privacy protection of civil aviation corporations has become a challenging problem. Therefore, a blockchain-based flight operation data sharing scheme, named BFOD is designed to achieve the privacy protection and secure sharing of flight operation data. In BFOD, physical entities of airlines, airports and air traffic control are divided into data owners, data requesters and authorization institutes according to the business logic. First, the authorization institute grants a hash anonymous identity and different levels of access right for each civil aviation corporation. When the data are accessed, a hash anonymous identity is designed to verify the access right of the data requester, which protects identity privacy. Then, zero-knowledge succinct noninteractive argument of knowledge (zk-SNARKs) is employed to verify whether the flight operation data meets the specific requirements raised by the data requester without disclosing data privacy. Finally, a proxy re-encryption is used to improve the sharing efficiency of flight operation data. In addition, a grouped practical Byzantine fault tolerant (PBFT) algorithm is proposed to reduce consensus latency. The theoretical analysis and experimental results show that the consensus latency of the grouped PBFT algorithm reduces 91.4% under the number of consensus node$\text{N}=$100, and the BFOD meets data confidentiality, availability and privacy protection, which is feasible and efficient.
This study presents an architectural framework for the blockchain-based usage-based insurance (UBI) policy auction mechanism in the internet of vehicles (IoV) applications. The main objective of this study is to analyze and design the specific blockchain architecture and management considerations for the UBI environment. An auction mechanism is developed for the UBI blockchain platform to enhance consumer trust. The study identifies correlations between driving behaviors and associated risks to determine a driver's score. A decentralized bidding algorithm is proposed and implemented on a blockchain platform using elliptic curve cryptography and first-price sealed-bid auctions. Additionally, the model incorporates intelligent contract functionality to prevent unauthorized modifications and ensure that insurance prices align with the prevailing market value. An experimental study evaluates the system's efficacy by expanding the participant pool in the bidding process to identify the winning bidder and is investigated under scenarios where varying numbers of insurance companies submit bids. The experimental results demonstrate that as the number of insurance companies increases exponentially, the temporal overhead incurred by the system exhibits only marginal growth. Moreover, the allocation of bids is accomplished within a significantly abbreviated timeframe. These findings provide evidence that supports the efficiency of the proposed algorithm.
Bitcoin was released in 2008 as an electronic peer-to-peer payment system. The aim was to enable financial transactions in an anonymous manner between participating parties. However, Bitcoin is considered a pseudonymous network rather than anonymous, as the identity of the address owner is unknown, but every transaction is permanently stored on the Bitcoin blockchain and can be tracked by anyone. The structure of the blockchain, its transactions, and the Bitcoin network make it possible to deanonymize pseudonyms through various methods such as flow analysis, heuristics, and network traffic observations. Once a connection is established between pseudonyms used in the Bitcoin network and the real world, all previous transactions can be attributed to that identity. In 2021, Taproot was introduced to further increase privacy within the Bitcoin Network by introducing a new address format which will allow transactions to be more indistinguishable from one another. In this paper, we analyze current methods for deanonymizing Bitcoin transactions to understand which parts of the Bitcoin protocol they exploit. In addition, we look at the changes introduced by Taproot and determine the extent to which these changes affect the methods and what assumptions must be made for these methods to remain applicable.
Location-based services are at the heart of many applications that individuals use every day. However, there is often no guarantee of the truthfulness of users’ location data, since this information can be easily spoofed without a proof mechanism. In distributed system applications, preventing users from submitting counterfeit locations becomes even more challenging because of the lack of a central authority that monitors data provenance. In this work, we propose a decentralized architecture based on blockchains and decentralized technologies, offering a transparent solution for Proof of Location (PoL). We specifically address two main challenges, i.e., the issuing process of the PoL and the proof verification. We describe a smart contract based implementation in Reach, a blockchain-agnostic smart contract language, and the tests we conducted on different blockchains, i.e. Ethereum, Polygon, and Algorand, measuring latency and costs due to the payment of fees. Results confirm the viability of the proposal.
Prabhat Kumar, Randhir Kumar, Moayad Aloqaily, A.K.M. Najmul Islam
The next-generation digital revolution is anticipated to be the convergence of Consumer Internet of Things (CIoT) platforms and Metaverse. The use of Metaverse in CIoT can offer a hyper-spatiotemporal, self-sustaining 3D virtual shared space for people to interact, work, and play. Despite the hype around CIoT-inspired Metaverse, security and privacy concerns are seen as the two biggest obstacles in the communication infrastructure and information gathering procedures. The eXplainable Artificial Intelligence (XAI) and blockchain have the potential to reshape and transform the CIoT-inspired Metaverse by bringing significant enhancements in terms of explainability, interpretability, transparency, traceability, and immutability regarding data and communications. In this paper, we first discuss about the security and privacy issues in CIoT-inspired Metaverse. Second, we discuss the importance and properties of XAI and blockchain with a use case to demonstrate the benefits of our proposed architecture to tackle the aforementioned obstacles. Finally, we highlight the future research directions in building futuristic CIoT-inspired Metaverse.