Merkle Tree Research Articles

Secured Data Sharing Method for Wireless Communication Network Based on Digital Twin and Merkle Hash Tree

Secured Data Sharing Method for Wireless Communication Network Based on Digital Twin and Merkle Hash Tree

Picking the juiciest cherries from the blockchain tree: Is the hype still alive?

Abstract Over the last years, blockchain technology has been promoted to revolutionize industries through trustless transactions. However, it has recently faced scrutiny for its failure to deliver on its alleged potential. This paper shows how blockchain applications have fallen short of their claims, including trustless and fraud-less transactions, as well as transparency systems. Building on this analysis, this research inspects how Blockchain’s building blocks, such as the Merkle tree and digital signatures, are still highly relevant technological concepts since their introduction decades ago. The paper examines these aspects, the unfulfilled claims, and the salvageable features through a critical analysis, offering insights into various sectors and jurisdictions, using as an example case a public project developed in Colombia. From cases of wrongful implementations, the paper demonstrates the necessity to put specific needs and requirements of real-world applications before the urge to apply blockchain technology without diligent social analysis, especially considering public funds.

Cooperative networking and information processing system of wireless communication UAV under the background of intelligent service

AbstractIn order to solve the huge impact of the digital information age on many technical and industrial fields, a periodic fast search genetic algorithm is proposed. Based on the reconnaissance mission, this paper introduces the common allocation strategy into mission planning, and constructs the mathematical model of multi unmanned aerial vehicle (UAV) cooperative reconnaissance mission planning decision‐making. The proposed periodic fast search genetic algorithm is used to solve the problem of multi UAV cooperative reconnaissance mission planning. In 2020, the industry growth rate of global UAV technology expenditure was as high as 30.6%, and the compound growth rate of UAV in China reached 63.5%, which is enough to see the great prospect of the integrated development of UAV technology and different industries. The experiment evaluates the log verification module of UAV by comparing the two data structures of Merkle tree and linear, and the time and memory overhead of storing and verifying logs, which shows the effectiveness of the log verification scheme in this paper.

Enhancing MQTT security for internet of things: Lightweight two-way authorization and authentication with advanced security measures

The MQTT (Message Queuing Telemetry Transport) protocol used the pub-sub model for IoT communication. Bolstering security with two-way authentication becomes a critical necessity. This paper presents a novel algorithm that fortifies MQTT with enhanced security measures, utilizing a refined MQTT implementation that integrates with a Merkle tree. HBMQTT Broker increases security as it uses different plugins, like the authentication plugin and the authorization plugin. These plugins serve to add extra layers of protection, reinforcing security protocols and heightening resilience to potential threats. The Merkle tree integration enhances data security during data transmission, effectively distinguishing between authentic and inauthentic data streams. Merkle trees generate tokens, which are used for secure data transmission. The algorithm is tested with four different hacking adversarial attacks: man-in-The-Middle (MITM), malware attack, denial of service (DoS), and phishing attack. Space and time complexity are also calculated for these attacks.

Implementing Quantum Resistant Algorithm in Blockchain-Based Applications

With quantum computing evolving very fast as we speak, the security and integrity of blockchain-based applications will become the most crucial aspect. A proposal is raised to use blockchain technology as a platform for writing and probating ‘wills’. Blockchain technology in drafting and probating wills makes them safe from manipulations, highly secure, and transparent. It also dramatically decreases the time required without catering for the challenges created by the current system. [9] This paper presents a new method for will transfer and inheritance management by implementing quantum-resistant algorithms in the security architecture of a blockchain decentralized application (DApp). The system uses IPFS Network for data storage and quantum-safe algorithms as retrieval and sending algorithms. The system includes Quantum-Resistant Dilithium Signatures and Merkle trees as the fundamental components for safeguarding the transfers of assets and claims for inheritance. Quantum-Resistant Dilithium Signatures offer an unbreakable shield against quantum attacks that are expected to happen, which in turn safeguards the privacy and authenticity of transactions. While Merkle trees are responsible for the organization of inheritance claims in an effective and tamper-proof manner, the introduced system incorporates smart contracts to address the execution of an inheritance case, adding more security and automation to the asset distribution process. The system ensures a robust security framework by integrating quantum-resistant algorithms at the very core of the blockchain DApp for instance, retrieval and sending. This research is of great significance to blockchain technology which is the emerging technology of the future because it addresses the existing threat of quantum computing by showing the feasibility of using quantum-resistant algorithms in practical applications. As established by the findings, besides Quantum-Resistant Dilithium Signatures and Merkle trees, the systems of asset transfers and inheritance management within blockchain networks are enhanced in terms of safety and reliability. Hence, paving the road to the creation of more secure and trustworthy digital asset management systems.

An Example of Parallel Merkle Tree Traversal: Post-Quantum Leighton-Micali Signature on the GPU

The hash-based signature (HBS) is the most conservative and time-consuming among many post-quantum cryptography (PQC) algorithms. Two HBSs, LMS and XMSS, are the only PQC algorithms standardised by the National Institute of Standards and Technology (NIST) now. Existing HBSs are designed based on serial Merkle tree traversal, which is not conducive to taking full advantage of the computing power of parallel architectures such as CPUs and GPUs. We propose a parallel Merkle tree traversal (PMTT), which is tested by implementing LMS on the GPU. This is the first work accelerating LMS on the GPU, which performs well even with over 10,000 cores. Considering different scenarios of algorithmic parallelism and data parallelism, we implement corresponding variants for PMTT. The design of PMTT for algorithmic parallelism mainly considers the execution efficiency of a single task, while that for data parallelism starts with the full utilisation of GPU performance. In addition, we are the first to design a CPU-GPU collaborative processing solution for traversal algorithms to reduce the communication overhead between CPU and GPU. For algorithmic parallelism, our implementation is still 4.48 × faster than the ideal time of the state-of-the-art traversal algorithm. For data parallelism, when the number of cores increases from 1 to 8192, the parallel efficiency is 78.39%. In comparison, our LMS implementation outperforms most existing LMS and XMSS implementations.

Analyzing FOSS license usage in publicly available software at scale via the SWH-analytics framework

The Software Heritage (SWH) dataset represents an invaluable source of open-source code as it aims to collect, preserve, and share all publicly available software in source code form ever produced by humankind. Although designed to archive deduplicated small files thanks to the use of a Merkle tree as the underlying data structure, querying the SWH dataset presents challenges due to the nature of these structures, which organize content based on hash values rather than any locality principle. The magnitude of the repository, coupled with the resource-intensive nature of the download process, highlights the need for specialized infrastructure and computational resources to effectively handle and study the extensive dataset housed within SWH. Currently, there is a lack of infrastructures specifically tailored for running analytics on the SWH dataset, leaving users to handle these issues manually. To address these challenges, we implemented the SWH-Analytics (SWHA) framework, a development environment that transparently runs custom analytic applications on publicly available software data preserved over time by SWH. Specifically, this work shows how SWHA can be effectively exploited to study usage patterns of free and open-source software licenses, highlighting the need to improve license literacy among developers.

Effective privacy preserving in cloud computing using position aware Merkle tree model

In this research manuscript, a new protocol is proposed for predicting the available space in the cloud and verifying the security of stored data. The protocol is utilized for learning the available data, and based on this learning, the available storage space is identified, after which the cloud service providers allow for data storage. The Integrity verification separates the private and the public data, which avoids privacy issues. The integration of the private data is done with the help of cloud service providers with respect to the third-party auditing (TPA). Earlier, public key cryptography and bilinear map technologies have been combined by the researchers, but the computation time and costs were high. To secure the integrity of the data storage, the client should execute several computations. Therefore, this research suggests a reliable and effective method called position-aware Merkle tree (PMT), which is implemented for ensuring data integrity. The proposed system uses a PMT that enables the TPA to perform multiple auditing tasks with high efficiency, less computational cost and computation time. Simulation results clearly shows that the developed PMT method consumed 0.00459 milliseconds of computation time, which is limited when compared to the existing models.

VADIA-Verkle Tree-based Approach for Dealing Data Integrity Attacks in Opportunistic Mobile Social Networks

Opportunistic Mobile Social Networks (OMSN) are prone to data integrity attacks that jeopardize the integrity of the routing data inside the network. Among the several techniques that cope with these attacks in OMSN, tree-based approaches have proven to be the most effective due to its ease of data verification and ensurance in data integrity. This paper evaluates two tree-based data structures, Merkle tree and Verkle tree in terms of their effectiveness in detecting and preventing such attacks. The evaluation considers tree-generation time and proof-checking time, and the results demonstrate that the Verkle tree is a bandwidth-efficient solution and have lower proof-checking time, with a reduction of 98.33% than Merkle tree. This makes Verkle tree a good choice for handling data integrity attacks in OMSN. A Verkle tree-based approach, named VADIA, is proposed to handle data integrity attacks such as packet dropping, packet modification and pollution attack in OMSN. The proposed approach is implemented in the Opportunistic Network Environment (ONE) simulator and is shown to be effective in detecting malicious nodes and paths, reducing false negative rates, and improving accuracy in detecting malicious activities. The results demonstrate a 47%, 84% and 69% improvement in malicious node, malicious path and malicious activity detection over a period of time. Furthermore, the approach achieves an 80% reduction in false negative rates.

A Comprehensive Approach to User Delegation and Anonymity within Decentralized Identifiers for IoT.

Decentralized Identifiers have recently expanded into Internet of Things devices and are crucial in securing users' digital identities and data. However, Decentralized Identifiers face challenges in scenarios necessitating authority delegation and anonymity, such as when dealing with legal guardianship for minors, device loss or damage, and specific medical contexts involving patient information. This paper aims to strengthen data sovereignty within the Decentralized Identifier system by implementing a secure authority delegation and anonymity scheme. It suggests optimizing verifiable presentations by utilizing a sequential aggregate signature, a Non-Interactive Zero-Knowledge Proof, and a Merkle tree to prevent against linkage and Sybil attacks while facilitating delegation. This strategy mitigates security risks related to delegation and anonymity, efficiently reduces the computational and verification efforts for signatures, and reduces the size of verifiable presentations by about 1.2 to 2 times.

ECo-Bag: An elastic container based on merkle tree as a universal digital evidence bag

Unique traits generated automatically or artificially, such as firewall logs, OS event logs, and various metadata, are well hidden in the digital evidence that cannot be easily perceived by the investigator in some cases. Digital data is invisible, and it is necessary that attention is focused on traditional management with integrity because of the involvement of various stakeholders in the secure preservation and analysis of the forensic process. Similar to file formats, digital evidence bags (DEB), such as E01 and L01, are widely used to contain digital data for certain facilities in a raw format, which also include metadata. The DEB can provide a way to obtain data through selective imaging, extracting and collecting only the parts necessary from the extensive data for proof. However, it cannot flexibly handle information obtained from large amounts of data or when sensitive data is involved or destroy superfluous materials that must be protected. Therefore, in this study, we propose a new container format based on the Merkle tree, which is used as a universal DEB. The proposed ECo-Bag can store physical and logical images from the storage medium, bit streams transmitted over networks, file segments in the cloud or distributed system, secondary outcomes, and metadata. Furthermore, it can support operations to destruct or seal the data initially collected while verifying the data integrity and tracking the provenance within the chain of custody. Thus, it is expected to contribute to the elastic management of addition and deletion of evidence in digital investigation and e-discovery.

Authenticating Account Balances in the Bitcoin Ecosystem via Merkle Trees

Bitcoin, as the trailblazing cryptocurrency, has profoundly impacted global markets and stands as a formidable contender to traditional financial paradigms. Yet, even within the sophisticated framework of Bitcoin, there remains potential for enhancement. Within the Bitcoin ecosystem, lightweight nodes operate without storing all transaction details. Instead, they retain only the block headers' content and specific transactional data pertinent to their own operations. To authenticate a transaction's presence in the blockchain, these lightweight nodes seek a Merkle proof from the full nodes. However, a challenge arises when lightweight nodes aim to validate the accuracy of their account balances sourced from full nodes. The latter relies on a data structure known as the Unspent Transaction Outputs (UTXO) for streamlined balance computation. In contrast, lightweight nodes grapple with ascertaining the veracity of such balance calculations. A viable solution lies in leveraging the available space in every block's coinbase transaction, which permits arbitrary modifications. By arranging the UTXO within a Merkle tree structure and embedding its root into the coinbase transaction's available space, account balance verification for lightweight nodes can be significantly enhanced using the Merkle proof. While such modifications to the Bitcoin protocol might trigger soft forks, the risk of hard forks remains absent.

DRC-EDI: An integrity protection scheme based on data right confirmation for mobile edge computing

As far as mobile edge computing is concerned, it is necessary to ensure the data integrity of latency-sensitive applications during the process of computing. While certain research programs have demonstrated efficacy, challenges persist, including the inefficient utilization of computing resources, network backhaul issues, and the occurrence of false-negative detections. To solve these problems, an integrity protection scheme is proposed in this paper on the basis of data right confirmation (DRC). Under this scheme, a two-layer consensus algorithm is developed. The outer algorithm is applied to establish a data authorization mechanism by marking the original data source to avoid the false negative results caused by network attacks from the data source. In addition, blockchain-based mobile edge computing (BMEC) technology is applied to enable data sharing in the context of mobile edge computing while minimizing the network backhaul of edge computing. Based on the Merkle Tree algorithm, the inner layer algorithm is capable not only of accurately locating and promptly repairing damaged data but also of verifying all servers in the mobile edge computing network either regularly or on demand. Finally, our proposal is evaluated against two existing research schemes. The experimental results show that our proposed scheme is not only effective in ensuring data integrity in mobile edge computing, but it is also capable of achieving better performance.

Revocable and verifiable weighted attribute-based encryption with collaborative access for electronic health record in cloud

The encryption of user data is crucial when employing electronic health record services to guarantee the security of the data stored on cloud servers. Attribute-based encryption (ABE) scheme is considered a powerful encryption technique that offers flexible and fine-grained access control capabilities. Further, the multi-user collaborative access ABE scheme additionally supports users to acquire access authorization through collaborative works. However, the existing multi-user collaborative access ABE schemes do not consider the different weights of collaboration users. Therefore, using these schemes for weighted multi-user collaborative access results in redundant attributes, which inevitably reduces the efficiency of the ABE scheme. This paper proposes a revocable and verifiable weighted attribute-based encryption with collaborative access scheme (RVWABE-CA), which can provide efficient weighted multi-user collaborative access, user revocation, and data integrity verification, as the fundamental cornerstone for establishing a robust framework to facilitate secure sharing of electronic health records in a public cloud environment. In detail, this scheme employs a novel weighted access tree to eliminate redundant attributes, utilizes encryption version information to control user revocation, and establishes Merkle Hash Tree for data integrity verification. We prove that our scheme is resistant against chosen plaintext attack. The experimental results demonstrate that our scheme has significant computational efficiency advantages compared to related works, without increasing storage or communication overhead. Therefore, the RVWABE-CA scheme can provide an efficient and flexible weighted collaborative access control and user revocation mechanism as well as data integrity verification for electronic health record systems.

A blockchain privacy-conserving framework for secure medical data transmission in the internet of medical things

The Internet of Medical Things (IoMT) has transformed healthcare, collecting and transmitting vast medical data. This study proposes an innovative solution, integrating blockchain into IoMT within a fog-cloud computing framework for secure medical data transmission. The blockchain-based zero-trust system ensures reliable data auditing and Electronic Health Repository (EHR) protection. New blockchain entries atop prior blocks deter tampering, with Quad Merkle tree and zero-knowledge proof encryption ensuring data integrity and privacy. The multi-critic deep deterministic policy gradient algorithm optimizes task-offloading decisions in the fog-cloud layer. Security analyses validate its effectiveness, improving computing efficiency and ensuring data fidelity and privacy. These findings position the system as a promising solution for enhancing medical data security and integrity in edge-fog cloud environments, aligning with evolving healthcare technology demands.

Secure and efficient public auditing system of user data using hybrid AES-ECC crypto system with Merkle hash tree in blockchain

Secure and efficient public auditing system of user data using hybrid AES-ECC crypto system with Merkle hash tree in blockchain

Integrating Merkle Trees with Transformer Networks for Secure Financial Computation

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accuracy, and F1 score. In particular, in the task of stock price prediction, the performance is notable, with nearly all evaluation metrics scoring above 0.9. Moreover, the performance of the model across various hardware platforms, as well as the security performance of the proposed method, were investigated. The Merkle-Transformer exhibits exceptional performance and robust data security even in resource-constrained environments across diverse hardware configurations. This research offers a new perspective, underscoring the importance of considering data security in financial data processing and confirming the superiority of integrating data verification mechanisms in deep learning models for handling financial data. The core contribution of this work is the first proposition and empirical demonstration of a financial data analysis model that fuses data integrity verification with efficient data processing, providing a novel solution for the fintech domain. It is believed that the widespread adoption and application of the Merkle-Transformer model will greatly advance innovation in the financial industry and lay a solid foundation for future research on secure financial data processing.

Enhanced scalability and privacy for blockchain data using Merklized transactions

Blockchain technology has evolved beyond the use case of electronic cash and is increasingly used to secure, store, and distribute data for many applications. Distributed ledgers such as Bitcoin have the ability to record data of any kind alongside the transfer of monetary value. This property can be used to provide a source of immutable, tamper-evident data for a wide variety applications spanning from the supply chain to distributed social media. However, this paradigm also presents new challenges regarding the scalability of data storage protocols, such that the data can be efficiently accessed by a large number of users, in addition to maintaining privacy for data stored on the blockchain. Here, we present a new mechanism for constructing blockchain transactions using Merkle trees comprised of transaction fields. Our construction allows for transaction data to be verified field-wise using Merkle proofs. We show how the technique can be implemented either at the system level or as a second layer protocol that does not require changes to the underlying blockchain. This technique allows users to efficiently verify blockchain data by separately checking targeted individual data items stored in transactions. Furthermore, we outline how our protocol can afford users improved privacy in a blockchain context by enabling network-wide data redaction. This feature of our design can be used by blockchain nodes to facilitate easier compliance with regulations such as GDPR and the right to be forgotten.

Anti-Software Attack Ear Identification System Using Deep Feature Learning and Blockchain Protection

Ear recognition has made good progress as an emerging biometric technology. However, the recognition performance, generalization ability, and feature robustness of ear recognition systems based on hand-crafted features are relatively poor. With the development of deep learning, these problems have been partly overcome. However, the recognition performance of existing ear recognition systems still needs to be improved when facing unconstrained ear databases in realistic scenarios. Another critical problem is that most systems with ear feature template databases are vulnerable to software attacks that disclose users’ privacy and even bring down the system. This paper proposes a software-attack-proof ear recognition system using deep feature learning and blockchain protection to address the problem that the recognition performance of existing systems is generally poor in the face of unconstrained ear databases in realistic scenarios. First, we propose an accommodative DropBlock (AccDrop) to generate drop masks with adaptive shapes. It has an advantage over DropBlock in coping with unconstrained ear databases. Second, we introduce a simple and parameterless attention module that uses 3D weights to refine the ear features output from the convolutional layer. To protect the security of the ear feature template database and the user’s privacy, we use Merkle tree nodes to store the ear feature templates, ensuring the determinism of the root node in the smart contract. We achieve Rank-1 (R1) recognition accuracies of 83.87% and 96.52% on the AWE and EARVN1.0 ear databases, which outperform most advanced ear recognition systems.

Data structure and privacy protection analysis in big data environment based on blockchain technology

In today's digital world, the rapid advancement of Information Technology (IT) has made it crucial to prioritize the protection and management of data storage and retrieval. It is vital to challenge the difficulties related to dispersed and decentralized data to build strong mechanisms for access control and provide effective authorization and authentication in data processing. In the contemporary context of IT, the imperative to secure data storage and retrieval has become distinctly observed. The challenges posed by distributed and decentralized data demand the development of robust mechanisms for access control, demanding a focus on proper authorization and authentication in transaction processing. This research addresses the existing gap by comprehensively adapting data structures effectively to the evolving needs of secure access and storage control. It uses the Enhanced Merkle Tree (EMT) as a novel data structure. This article initially modifies the conventional Merkle Tree (MT) structure used in Blockchain technology to suit e-healthcare Systems (e-HS) requirements. The EMT enhances data security in access and storage and significantly improves data integrity management. Its constant three-degree MT with multiple leaves, branches, and a single root node enables updated data authentication, verification, and validation procedures. The proposed method is applied to the e-HS scenario, and the proposed EMT outperforms existing state-of-the-art techniques, achieving a minimal verification time of 14.26 m for 100 transactions. This research, therefore, contributes to the discourse on data security by presenting an innovative and efficient solution tailored to the unique challenges of e-health systems.