As the usage of artificial intelligence (AI) grows within the field of cybersecurity, so too does the demand for secure and decentralized AI systems to protect against potential cyber threats. Blockchain technology (BT) has emerged as the ideal approach for increasing both the security and privacy of AI systems since it provides decentralized and immutable data storage. This systematic literature review focuses on the integration of BT with decentralized AI within cybersecurity. It provides a comprehensive taxonomy of BT and decentralized AI for cybersecurity serving as the starting point for the study. This paper begins with an overview of BT and its possible uses in cybersecurity and also analyzes its challenges and opportunities. Decentralized AI is also covered in the study, along with its potential advantages and difficulties in decentralized AI cybersecurity. Building on that foundation, this study provides convincing findings highlighting the beneficial relationships between BT and decentralized AI for cybersecurity, contributing to a nuanced comprehension of their integration. Moreover, it offers real-world uses of blockchain-enabled decentralized AI to discuss its usefulness in tackling cybersecurity concerns. Finally, this paper discusses future research directions for blockchain-enabled decentralized AI in cybersecurity, including potential applications and implications of this technology in the field, emphasizing the potential of blockchain-enabled decentralized AI in cybersecurity to enhance security, privacy, and trust in AI systems.
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.
Sandro Amofa, Qi Xia, Hu Xia, Isaac Amankona Obiri · 7 authors
Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.
Blockchain has been recognized as a secure technology with which unique and tamper-proof certificates can be issued. Today the secure and immutable storage of vital records, such as birth certificates, is of paramount importance. Traditional systems for recording and managing such documents are complex, tedious, and inaccessible for many. At present, issuing a certificate involves three stakeholders; Parents, Hospitals, and Registrars. The Parents must visit the Registrar and report the birth after which the Registrar verifies the birth, which is a time-intensive process for the Registrar. To address these challenges, we present a web application that leverages blockchain technology to record and manage birth certificates. By utilizing a decentralized and distributed ledger, our application improves accessibility, transparency, and increased security. This paper outlines the design, implementation, and evaluation of our birth certificate recording system, highlighting its benefits for hospitals, registrars, and individuals. Furthermore, limitations and the future work of this technology are discussed.
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 widespread use of UAVs in smart cities for tasks like traffic monitoring and environmental data collection creates significant privacy and security concerns due to the transmission of sensitive data. Traditional UAV-MEC systems with centralized data processing expose this data to risks like breaches and manipulation, potentially hindering the adoption of these valuable technologies. To address this critical challenge, we propose UBFL, a novel privacy-preserving federated learning mechanism that integrates blockchain technology for secure and efficient data sharing. Unlike traditional methods relying on differential privacy (DP), UBFL employs an adaptive nonlinear encryption function to safeguard the privacy of UAV model updates while maintaining data integrity and accuracy. This innovative approach enables rapid convergence, allowing the base station to efficiently identify and filter out severely compromised UAVs attempting to inject malicious data. Additionally, UBFL incorporates the Random Cut Forest (RCF) anomaly detection algorithm to actively identify and mitigate poisoning data attacks. Extensive comparative experiments on benchmark datasets CIFAR10 and Mnist demonstrably showcase UBFL's effectiveness. Compared to DP-based methods, UBFL achieves accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%) in privacy preservation while maintaining superior accuracy. Notably, under data pollution scenarios with varying attack sample rates (10%, 20%, and 30%), UBFL exhibits exceptional resilience, highlighting its robust capabilities in securing UAV gradients within MEC environments.
Federated Learning is a revolutionary approach to machine learning. Its purpose is to enable multiple participants to collaboratively train machine learning models without the need to share local data. The main objective is to address issues related to data privacy and security. In traditional machine learning, data is typically centralized and stored in a single location or on cloud servers for training. However, this centralized training approach carries risks of potential data leakage, especially concerning sensitive and critical information. Industries such as healthcare and finance, which involve sensitive data, place a premium on safeguarding data privacy. Furthermore, in cases where data cannot be easily transferred or is subject to privacy regulations, centralized methods may face limitations. Federated Learning revolutionizes the conventional approach by introducing a decentralized model training process. It maintains data decentralization while achieving collaborative model optimization, greatly enhancing data privacy and security. The MOON algorithm, an integral part of federated learning, contributes to its novelty. As a significant component, the MOON algorithm facilitates new possibilities for federated learning. In this article, the research will elaborate on the MOON algorithm within the context of federated learning. And this article will delve into its description and optimization, elucidating how it enhances federated learning.
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.
Blockchain is a distributed ledger technology that possesses characteristics such as decentralization, tamper resistance, and programmability. However, while blockchain ensures transaction openness and transparency, transaction privacy is also at risk of being exposed. Therefore, this paper proposes the blockchain transaction privacy protection method based on the stealth address and the note mechanism to address the privacy leakage risk in blockchain public environments. Firstly, the proposed method generates a random seed known only to the parties involved based on the Diffie–Hellman key exchange protocol, ensuring the privacy of transactions. Then, it utilizes the Note Commitments table to maintain the binding relationship between the stealth address and the corresponding note, enabling efficient transfer and verification of note ownership. The uniqueness of the stealth address is utilized as an invalidation identifier for notes in the Nullifier table, ensuring efficient verification of the correctness of note invalidation identifiers. Additionally, this method employs Pedersen commitment and Bulletproofs range proof to generate proof of the legality of transaction amounts, enabling the concealment of transaction amounts and facilitating private transactions between the parties involved. Finally, this paper presents a detailed performance analysis, implementation, and testing of the method. From the results, it can be concluded that the method proposed can effectively prevent fraudulent behavior by various transaction participants and ensure the security, privacy, and integrity of the transaction. Critical processes consume only milliseconds, and the related commitments and proofs are also minimal, which is crucial for controlling transaction costs. At the same time, this method achieves a completely decentralized privacy transaction solution.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Our cyber security learning system involves an interactive environment for students to practice executing different attack and defense techniques relating to cyber security concepts. We intend to use a blockchain database to secure data from this learning system. The data being secured are students' scores accumulated by successful attacks or defends from the other students' implementations. As more professionals are departing from traditional relational databases, the enthusiasm around distributed ledger databases is growing, specifically blockchain. With many available platforms applying blockchain structures, it is important to understand how this emerging technology is being used, with the goal of utilizing this technology for our learning system. In order to successfully secure the data and ensure it is tamper resistant, an investigation of blockchain technology use cases must be conducted. In addition, this paper defined the primary characteristics of the emerging distributed ledgers or blockchain technology, to ensure we effectively harness this technology to secure our data. Moreover, we explored using a blockchain database for our data.
This paper introduces the zk-IoT framework, a novel approach to enhancing the security of Internet of Things (IoT) ecosystems through the use of Zero-Knowledge Proofs (ZKPs) on blockchain platforms. Our framework ensures the integrity of firmware execution and data processing in potentially compromised IoT devices. By leveraging the concept of ZKP, we establish a trust layer that facilitates secure, autonomous communication between IoT devices in environments where devices may not inherently trust each other. The framework includes zk-Devices, which utilize functional commitment to generate proofs for executed programs, and service contracts for encoding interaction logic among devices. It also utilizes a blockchain layer and a relayer as a ZKP storage and data communication protocol, respectively. Our experiments demonstrate that proof generation, reading, and verification take approximately 694, 5078, and 19 milliseconds in our system setup, respectively. These timings meet the practical requirements for IoT device communication, demonstrating the feasibility and efficiency of our solution. The zk-IoT framework represents a significant advancement in the realm of IoT security, paving the way for reliable and scalable IoT networks across various applications, such as smart city infrastructures, healthcare systems, and industrial automation.
Shrabani Sutradhar, S. P. Majumder, Rajesh Bose, Haraprasad Mondal · 5 authors
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.
To ensure the aggregation of a high-quality global model during the data-sharing process in the Internet of Vehicles (IoV), current approaches primarily utilize gradient detection to mitigate malicious or low-quality parameter updates. However, deploying gradient detection in plain text neglects adequate privacy protection for vehicular data. This paper proposes the IoV-BDSS, a novel data-sharing scheme that integrates blockchain and hybrid privacy technologies to protect private data in gradient detection. This paper utilizes Euclidean distance to filter the similarity between vehicles and gradients, followed by encrypting the filtered gradients using secret sharing. Moreover, this paper evaluates the contribution and credibility of participating nodes, further ensuring the secure storage of high-quality models on the blockchain. Experimental results demonstrate that our approach achieves data sharing while preserving privacy and accuracy. It also exhibits resilience against 30% poisoning attacks, with a test error rate remaining below 0.16. Furthermore, our scheme incurs a lower computational overhead and faster inference speed, markedly reducing experimental costs by approximately 26% compared to similar methods, rendering it suitable for highly dynamic IoV systems with unstable communication.
Tácito Augusto Farias Júnior, Rafael Oliveira Vasconcelos, Admilson de Ribamar Lima Ribeiro
Privacy protection ensures that individuals have control over personal data, preventing abuse and preserving trust in the use of online services. In the “Digital Era”, where the collection, storage and processing of personal information have become ubiquitous, data privacy emerges as a relevant topic. In this sense, laws were created, such as the General Data Protection Law (LGPD) in Brazil and the General Data Protection Regulation (GDPR) in Europe, to control privacy and the processing of personal data. The article presents a comparative analysis of 2 (two) data privacy mechanisms, the Zero-Knowledge Proof (ZKP) and Ring Signatures, used in Blockchain, aiming at the legal and regulatory implications with the LGPD and GDPR. The comparative study between ZKP and Ring Signatures highlights the flexibility of ZKP in various contexts, including voting and secure authentication systems, while Ring Signatures offer significant advantages in terms of scalability and efficiency in systems where subscriber anonymity is considered fundamental. Furthermore, the legal and regulatory implications of the ZKP are discussed, mainly in relation to LGPD and GDPR. Finally, the article concludes that the comparative analysis offers insights into applications, challenges and legal and regulatory implications, particularly in relation to data privacy and compliance with regulations such as LGPD and GDPR.
This paper explores the relationship between the development of the internet and health care, highlighting their parallel growth and mutual influence. It delves into the transition from the early, static days of Web 1.0, akin to siloed physician expertise in health care, to the more interactive and patient-centric era of Web 2.0, which was accompanied by advancements in medical technologies and patient engagement. This paper then focuses on the emerging era of Web3-the decentralized web-which promises a transformative shift in health care, particularly in how patient data are managed, accessed, and used. This shift toward Web3 involves using blockchain technology for decentralized data storage to enhance patient data access, control, privacy, and value. This paper also examines current applications and pilot projects demonstrating Web3's practical use in health care and discusses key questions and considerations for its successful implementation.
Abstract Internet of Medical Things (IoMT) has typical advancements in the healthcare sector with rapid potential proof for decentralised communication systems that have been applied for collecting and monitoring COVID‐19 patient data. Machine Learning algorithms typically use the risk score of each patient based on risk factors, which could help healthcare providers decide about post‐COVID‐19 care and follow‐up where the data privacy is another severe concern. The authors investigate the applicability of a distributed reinforcement learning approach in a Federated Learning (FL) multi‐disciplinary reinforcement system and explores the potential benefits of incorporating Blockchain Technology (BT) in the distributed system. Intermediate dependency features and transactions are avoided by applying Blockchain‐enabled reinforcement FL for the post‐COVID‐19 patient data of IoMT applications. The proposed approach helps to improvise clinical monitoring and ensure secure communication and data privacy in a decentralised manner. The main objective is to improve the efficiency and scalability of the reinforcement FL process in a distributed environment while ensuring data privacy and security through BT for IoMT applications. Results show that proposed approach achieve comparatively high reliability and outperforms the existing approaches.
Through the development of the metaverse concept from the Sumerian myth (5500 - 1800 BC) and mind-altering novel, “Snow Crash” in 1992, to today’s information age, human- and society-centred urban metaverse worlds, an extension of residents and urban society where the virtual and the physically real blend and are more organically integrated, are meant to mirror the fabric of urban life with no harm to their residents. The success of urban metaverse cybercommunities depends on the quality of data-driven Smart City (SC) Digital Twins (DTs), the seamless exchange of data between cyber and physical worlds (e.g. between residents and their counterpart “Avatars’’) and the processing of the data effectively and efficiently with no vicious interventions. The potential risks in this ecosystem that incorporates Web3 can be extremer than the ones in Web2 since users are immersed with multiple tightly coupled wearable sensor-rich devices perceiving the blend of the real and the virtual with possible imminent negative experiences. This study, by analysing potential cyberthreats in the urban metaverse cyberspaces, proposes a blockchain-based Decentralised Privacy-Preserving Machine Learning (DPPML) authentication and verification technique, which uses the metaverse immersive devices and can be instrumented effectively against identity impersonation and theft of credentials, identity, or avatars.
To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility.
To address the challenges of low credibility, difficult data sharing, and regulatory supervision issues involving electronic evidence storage in the judicial preservation process, this paper proposes a blockchain-based judicial evidence preservation scheme. The scheme utilizes the characteristics of blockchain's immutability to achieve credible forensics of electronic evidence on the chain and employs the decentralized storage of the interplanetary file system for secure and efficient off-chain storage. Simultaneously, it resolves the problem of declining throughput due to limited block capacity. Additionally, it leverages smart contract technology to encompass major aspects of the judicial process, including user case registration, authority management, judicial evidence uploading and downloading, case data sharing, partial disclosure of case information, and regulatory review. Simulation experiments demonstrate that the scheme significantly improves throughput and stability. Performance tests indicate that the transfer speed of the interplanetary file system can meet the data-sharing needs among judicial organizations.
Federated Learning (FL) has presented one of the radical paradigms that permit collaborative models to be trained without centralized sensitive data, thereby supporting both privacy and compliance-related concerns. Nevertheless, issues involving communication bottlenecks, non-iid data distribution, and privacy leak channels continue to impede its general use. This research project puts forward an integrated approach integrating FL with Agentic Intelligence (AIgI), forming a decentralized, adapting framework that integrates self-governing decision-making capacity, dynamic coordination and enhanced optimized resources. The framework utilizes sophisticated FL optimization techniques, such as FedYogi, to improve convergence rate and model precision and the Communication-Aware Federated Learning (CA-FL) technique to minimize the bandwidth usage. An assessment of privacy risks is performed with the help of the FedInverse tool, which shows vulnerabilities to model inversion attacks and supports the idea of multi-layered defense mechanisms. As experimental results confirm, the proposed framework achieves excellent accuracy compared to conventional FedAvg, both in terms of precision, recall, and Dice similarity coefficient and scales to thousands of clients thanks to FEDn orchestration. The integration of agentic reasoning enables the system to be adaptive enough to cope with a heterogeneous environment, the lack of synchronous involvement of clients, and the trade-off between training during the optimization of network conditions. The presented work forms the basis of a new generation of privacy-preserving, decentralized AI architecture that is resilient, scalable, and apt to be used in sensitive applications, like healthcare, finance, and critical infrastructure.
Ensuring authorized access control in the IoT is vital for privacy and safety protection. Our study presents the novel IHIBE framework, which combines IOTA (a distributed ledger technology) with hierarchical identity-based encryption (HIBE), thereby enhancing both IoT security and scalability. This approach secures access tokens and policies while reducing the computational demand on data owners. Our empirical findings reveal a significant performance gap, with access rights delegation on the Raspberry Pi 4 exceeding those on AWS by over 250%. Moreover, our analysis uncovers optimal identity policy depths: up to 640 identities on AWS and 640 on the Raspberry Pi 4 for systems with higher tolerable delays, and 320 identities on AWS versus 160 on the Raspberry Pi 4 for systems with lower tolerable delays. The system shows practical viability, exhibiting insignificant operational time differences compared to Zhang et al.'s schemes, particularly in access rights verification processes, with a minimal difference of 33.35%. Our extensive security assessment, encompassing scenarios like encrypted token theft and compromise of authority, affirms the efficacy of our challenge-response and last-word challenge (LWC) mechanisms. This study underscores the importance of platform choice in IoT system architectures and provides insights for deploying efficient, secure, and scalable IoT environments.
Considering the privacy challenges of secure storage and controlled flow, there is an urgent need to realize a decentralized ecosystem of private blockchain for cyberspace. A collaboration dilemma arises when the participants are self-interested and lack feedback of complete information. Traditional blockchains have similar faults, such as trustlessness, single-factor consensus, and heavily distributed ledger, preventing them from adapting to the heterogeneous and resource-constrained Internet of Things. In this paper, we develop the game-theoretic design of a two-sided rating with complete information feedback to stimulate collaborations for private blockchain. The design consists of an evolution strategy of the decision-making network and a computing power network for continuously verifiable proofs. We formulate the optimum rating and resource scheduling problems as two-stage iterative games between participants and leaders. We theoretically prove that the Stackelberg equilibrium exists and the group evolution is stable. Then, we propose a multi-stage evolution consensus with feedback on a block-accounting workload for metadata survival. To continuously validate a block, the metadata of the optimum rating, privacy, and proofs are extracted to store on a lightweight blockchain. Moreover, to increase resource utilization, surplus computing power is scheduled flexibly to enhance security by degrees. Finally, the evaluation results show the validity and efficiency of our model, thereby solving the collaboration dilemma in the private blockchain.
Soil is an indispensable resource with critical implications in various fields such as agriculture, environmental science, climate change, hydrology, ecology, and geoscience. Accuracy and accessibility of soil data are crucial for informed decision making. However, the sharing and harmonization of soil data present significant challenges, particularly owing to the lack of a comprehensive identification system that ensures privacy and stewardship in a federated data sharing framework. Moreover, the inherent heterogeneity of soil properties across space and time complicates the establishment of connections between soil profiles and their corresponding properties. To address these challenges, a novel and persistent soil-data identifier, called SoilPrint, akin to a fingerprint, was proposed. SoilPrint utilizes a mathematical algorithm to effectively integrate the properties of soil profile layers (SPLP) with Geohashes, providing an efficient solution. The incorporation of SoilPrint streamlines the data federation process within a secure and distributed ledger, eliminating the need for complex data mapping or alignment. This approach ensures data privacy throughout the sharing process and addresses concerns associated with data management. To demonstrate the practical applications of SoilPrint, a case study using soil data from Ontario, Canada was presented. The results underscored the unique identification capabilities of SoilPrint for soil profiles and their associated properties, establishing it a promising tool for soil data management. SoilPrint facilitates data tracking, reuse, and analysis, thereby enhancing the efficiency and effectiveness of soil-related research and decision-making processes.
In today’s digital landscape, the exponential growth of data heightens security risks associated with traditional centralized storage systems. Utilizing blockchain technology, a shift towards decentralized data storage provides a more secure and private alternative. Central to our work is the exploration of symmetry in data management, a concept woven into the fabric of our proposed solution to challenge the inherency in InterPlanetary File System (IPFS) technology. Through the strategic utilization of smart contract-invoked random functions, our blockchain-based solution fragments and securely stores data in order to ensure a symmetrical balance between confidentiality and integrity. Our research endeavors are to contribute a robust, ethically grounded data storage framework fostering advancements in secure data sharing. The implications of this paper are significant in addressing contemporary challenges of data management within the expansive realm of big data.