Secure Data Storage Research Articles

Blockchain adoption and operational performance: A secondary data analysis on effects and contingencies

PurposeThis study analyzes the performance implications of adopting blockchain to support supply chain business processes. The technology holds as many promises as implementation challenges, so interest in its impact on operational performance has grown steadily over the last few years.Design/methodology/approachDrawing on transaction cost economics and the contingency theory, we built a set of hypotheses. These were tested through a long-term event study and an ordinary least squares regression involving 130 adopters listed in North America.FindingsCompared with the control sample, adopters displayed significant abnormal performance in terms of labor productivity, operating cycle and profitability, whereas sales appeared unaffected. Firms in regulated settings and closer to the end customer showed more positive effects. Neither industry-level competition nor the early involvement of a project partner emerged as relevant contextual factors.Originality/valueThis research presents the first extensive analysis of operational performance based on objective measures. In contrast to previous studies and theoretical predictions, the results indicate that blockchain adoption is not associated with sales improvement. This can be explained considering that secure data storage and sharing do not guarantee the factual credibility of recorded data, which needs to be proved to customers in alternative ways. Conversely, improvements in other operational performance dimensions confirm that blockchain can support inter-organizational transactions more efficiently. The results are relevant in times when, following hype, there are signs of disengagement with the technology.

A secure and lightweight cloud data deduplication scheme with efficient access control and key management

Data deduplication technology is extensively employed to enhance the storage efficiency of cloud servers by eliminating redundant files. Cloud users commonly encrypt their data prior to uploading it to the server. Conventional encryption algorithms, however, lead to the encryption of duplicated data from different users into distinct ciphertexts. Consequently, these ciphertexts must be stored in the cloud since the cloud server cannot identify such duplicated data. In this paper, we introduce a hybrid cloud-based secure deduplication scheme tailored for implementation on large-scale data systems. Specifically, our approach leverages ciphertext-policy attribute-based encryption (CP-ABE), which enables us to establish access control and key management via a private cloud server. Simultaneously, we leverage a public cloud server to cater to enterprises and groups seeking secure data storage. Notably, our approach ensures mutual zero-interaction verification between both public and private cloud servers through ElGamal encryption, thereby guaranteeing data unforgeability. The security assessment illustrates that our proposed approach ensures both data privacy and integrity. We also show that the approach resists brute-force attacks on the dictionary, prevents malicious users from deceiving cloud servers to return incorrect ciphertext, and achieves secure and efficient access control and key management. Furthermore, functional and performance evaluation underscores the superiority of our method over five other classical data deduplication schemes. Under the premise of having more comprehensive security settings, the performance of the scheme still maintains a good level at every stage.

Integrating NLP Chatbot into Career Guidance Web Application

Artificial Intelligence (AI) is the development of computer systems that can perform tasks that typically require human intelligence. The development of an advanced web-based chatbot system involves the integration of various technologies and methodologies to create a seamless conversational interface. Leveraging HTML, CSS, and JavaScript for frontend development, alongside Python Flask framework for backend functionality, the project aims to deliver a robust and intuitive user experience. Key to the system's operation is the incorporation of PyTorch for machine learning, allowing for the implementation of sophisticated natural language processing (NLP) techniques. Through tokenization and stemming using NLTK (Natural Language Toolkit), the system enhances its understanding of user queries, enabling it to provide relevant and contextually appropriate responses. At the core of the system lies a supervised learning model trained on a custom dataset compiled from user interactions. This model utilizes bag-of-words representations and neural network architectures to predict responses based on input text. By continuously refining its predictions through feedback mechanisms, the system improves its accuracy and responsiveness over time, ensuring a more tailored and effective user experience. Voice input functionality, facilitated by speech recognition technology, further enhances user interaction by allowing users to communicate with the chatbot using spoken commands. This feature expands the accessibility and convenience of the system, catering to a broader range of users with varying preferences and abilities. In addition to its conversational capabilities, the system prioritizes security and privacy, implementing robust user authentication mechanisms and secure data storage practices. By adhering to industry best practices and standards, the project aims to instill trust and confidence in users regarding the confidentiality and integrity of their data.User interface design and usability are also key considerations in the development process, with careful attention given to creating an intuitive and visually appealing interface. Through thoughtful design choices and user feedback, the system seeks to optimize the user experience and streamline the interaction flow, ensuring that users can easily navigate and utilize its features. Overall, the project endeavors to create an intelligent and user-friendly chatbot system that enhances communication and interaction between users and technology. By leveraging advanced technologies and methodologies, the system aims to provide personalized assistance and support to users, ultimately improving productivity, efficiency, and satisfaction in various domains and applications

Asynchronous verifiable information dispersal protocol of achieving optimal communication

The evolution of distributed systems necessitates robust protocols to facilitate secure and efficient data storage and retrieval amid challenges like asynchronous communication and adversarial threats. However, achieving the theoretical lower bounds of communication cost in Asynchronous Verifiable Information Dispersal (AVID) protocols remains an unresolved issue, prompting the need for innovation in this domain.In this paper, we propose a new AVID protocol designed to optimize communication cost. In contrast to previous approaches, we leverage vector commitment (VC) technology combined with erasure codes, replacing conventional hash tree structures or error correction codes. This approach results in significant O(logn) communication savings. Additionally, our research reveals that while ensuring simultaneous output from all honest nodes is desirable to satisfy Totality, it’s not necessary for all nodes to receive corresponding shares or proofs during the Dispersal phase. This insight allows us to circumvent the common O(n2κ) communication overhead.Our AVID protocol achieves an optimal dispersal cost of O(|M|+n2) compared to the prior best of O(|M|+κn2). Furthermore, each node in our protocol incurs a storage cost of O(|M|/n+κ) bits, while the retrieval cost per client is O(|M|+nκ).

Security Considerations in iOS App Development: Encryption, Authentication, and Secure Data Storage

Security Considerations in iOS App Development: Encryption, Authentication, and Secure Data Storage

Safety and Security of E-commerce Transactions Based on Blockchain Technology

The safety and security of e-commerce transactions are critical in today's digital landscape, where cyber risks abound. Blockchain technology's decentralized, transparent, and immutable ledger system offers a viable solution to these concerns. This study investigates the use of Distributed Ledger Technology (DLT), specifically Decentralized Identifiable Distributed Ledger Technology (DIDLT), in conjunction with the innovative Blockchain Consent Algorithm (BCA), to improve the safety and security of e-commerce transactions. Secure data storage and retrieval in e-commerce can be achieved by accurate digital signature production, key generation, blockchain building, and validation. This paper investigates how DIDLT, which combines decentralized identity management with blockchain technology, and BCA, a cutting-edge consensus algorithm designed for blockchain environments, work together to protect e-commerce transactions from identity theft, payment fraud, and data manipulation. This paper throws insight into DIDLT and BCA's potential to transform the e-commerce safety and security market by providing an in-depth analysis of their implementation. Using the incorporated DIDLT-BCA model significantly improves the safety effectiveness of the network, resulting in 98% security, shorter performance times of as much as 150 milliseconds, and mining times of up to 0.98 s.

The Information Technology (IT) Infrastructure of the Multicenter Archipelago of Ovarian Cancer Research Biobank: A Potential Blueprint for Other Biobanks.

Objective: Biobanks play a crucial role in fundamental and translational research by storing valuable biomaterials and data for future analyses. However, the design of their information technology (IT) infrastructures is often customized to specific requirements, thereby lacking the ability to be used for biobanks comprising other (types of) diseases. This results in substantial costs, time, and efforts for each new biobank project. The Dutch multicenter Archipelago of Ovarian Cancer Research (AOCR) biobank has developed an innovative, reusable IT infrastructure capable of adaptation to various biobanks, thereby enabling cost-effective and efficient implementation and management of biobank IT systems. Methods and Results: The AOCR IT infrastructure incorporates preexisting biobank software, mainly managed by Health-RI. The web-based registration tool Ldot is used for secure storage and pseudonymization of patient data. Clinicopathological data are retrieved from the Netherlands Cancer Registry and the Dutch nationwide pathology databank (Palga), both established repositories, reducing administrative workload and ensuring high data quality. Metadata of collected biomaterials are stored in the OpenSpecimen system. For digital pathology research, a hematoxylin and eosin-stained slide from each patient's tumor is digitized and uploaded to Slide Score. Furthermore, adhering to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, genomic data derived from the AOCR samples are stored in cBioPortal. Conclusion: The IT infrastructure of the AOCR biobank represents a new standard for biobanks, offering flexibility to handle diverse diseases and types of biomaterials. This infrastructure bypasses the need for disease-specific, custom-built software, thereby being cost- and time-effective while ensuring data quality and legislative compliance. The adaptability of this infrastructure highlights its potential to serve as a blueprint for the development of IT infrastructures in both new and existing biobanks.

Text Mining and Financial Analysis Modeling for Financial Statement Disclosure

This paper explores the integration of financial analysis, text mining, and blockchain technology to enhance financial statement disclosure and analysis processes. Leveraging text mining algorithms, the study extracts valuable insights from unstructured financial data, unveiling hidden patterns and sentiments within financial statements. Additionally, blockchain technology is employed to ensure secure and immutable storage of financial data, fostering transparency and trust in financial transactions and disclosures. Through the automation and digitization of financial reporting processes, stakeholders can streamline financial statement analysis, identify risks and opportunities, and make data-driven decisions with greater accuracy and efficiency.

Blockchain-Enabled Security Solutions for Medical Device Integrity and Provenance in Cloud Environments

The current period of medicine using digital technology for patient care presents a new level of integration of monitoring devices with the cloud computing environment that enables the collection, storage and access to data in ways that were never possible earlier. As the obvious part of this development, it is worth noting that the objective of such innovation is mostly on the integrity of data, provenance and security. Data integrity from as well as security of the Internet connected healthcare devices should be assured in the first place to keep patient safety and protect data privacy along with improve data-based decision-making. The centralized system and crowded nature of the current equipment are susceptible to single point of failure, data breach and potential manipulations of data, which raise questions and create doubts with regards data management processes pertaining to medical device systems. This work is addressed to the analysis of a novel security system based on blockchain that guarantees the implementation of a high performance with the solution of two medical device integrity and provenance safety issues in the cloud ecosystem. Fundamentally differentiating from the centralized systems that exist today, blockchain technology that is based on distributed database architectures, immutable logs, and consensus mechanisms provides for a new way to bring reliability and traceability to the entire medical device data chain. The suggested procedure is based on properties of blockchain technology. Such a solution can help to provide a clear and secure audit trail for medical devices. Storing, securing and accessing the device data can be carried out credibly, maintaining these data’s integrity and provenance. Ultimately, the solution, rely on the implementation of smart contracts, cryptocurrency processes, and the confidentiality and privacy of data, can be the answer which make up the practice of secure data sharing, data accessing and complying with regulations. The journal creates a modular system combining Medical devices, a cloud platform, and Blockchain solution. The architecture is intended to display the blockchain network's essential components, data validation and access control, and secure data storage mechanisms. Furthermore, the recommended solution implies state-of- the-art security tools, such as data encryption, access control, and abidance by regulatory systems, including HIPAA and GDPR. Implementation of an actual scenario of the proof-of-concept and performance evaluation are done to show the efficiency and performance of the blockchain-based solution provided. The results suggest that the proposed solution can establish the data reliability level, record all the various versions of modifications, and strengthen the security and transparency of medical device data processing in cloud computing. Through the exploration of the applications of blockchain for medical data management that this study proposes, we are laying the foundations of a future healthcare environment, which is expected to be more secure and trustworthy, where the sensor data of medical devices can be reliably controlled and accessed without jeopardizing the patient's safety or data privacy. To a great extent, the suggested solution can contribute to building trust in the digital tools utilized in health care, leading to more well-informed clinical decisions and ultimately improving the patients' results.

A Retrospective Analysis of Leadership, Awardees, and Member Gender Representation of the Canadian Society for Immunology.

The Canadian Society for Immunology (CSI) established a formal Equity, Diversity, and Inclusion (EDI) Committee with the goal of providing EDI advocacy and leadership within the CSI, as well as in the broader scientific community. A first task of this committee was to review the publicly available historical data on gender representation within the CSI's membership, leadership, award recipients, and conference chairs/presenters as a step in establishing a baseline reference point and monitoring the trajectory of future success in achieving true inclusion. We found that, except for overall membership and a specific subset of awards, all categories showed a historical bias toward men, particularly prior to 2010. Bias persists in various categories, evident even in recent years. However, we note an encouraging trend toward greater gender parity, particularly in the roles of President, symposium presenters, and workshop chairs, especially from 2017 onward. We present these findings as well as our recommendations to enhance inclusivity. These include a more comprehensive collection and secure storage of self-identification data, emphasis on EDI as an essential component of all annual meeting activities, and innovative measures of outreach, collaboration, and leadership with the aim of making the CSI a model for improving EDI in other professional research societies.

Application of Graph Theory for Blockchain Technologies

Blockchain technology, serving as the backbone for decentralized systems, facilitates secure and transparent transactional data storage across a distributed network of nodes. Blockchain platforms rely on distributed ledgers to enable secure peer-to-peer transactions without central oversight. As these systems grow in complexity, analyzing their topological structure and vulnerabilities requires robust mathematical frameworks. This paper explores applications of graph theory for modeling blockchain networks to evaluate decentralization, security, privacy, scalability and NFT Mapping. We use graph metrics like degree distribution and betweenness centrality to quantify node connectivity, identify network bottlenecks, trace asset flows and detect communities. Attack vectors are assessed by simulating adversarial scenarios within graph models of blockchain systems. Overall, translating blockchain ecosystems into graph representations allows comprehensive analytical insights to guide the development of efficient, resilient decentralized infrastructures.

Implementing the Triple-Data Encryption Standard for Secure and Efficient Healthcare Data Storage in Cloud Computing Environments

Implementing the Triple-Data Encryption Standard for Secure and Efficient Healthcare Data Storage in Cloud Computing Environments

Duplicate Detection on Cloud Data with Re-Encryption Based Data Sharing

Cloud data storage has gained immense popularity in recent years due to its scalability and accessibility. However, the efficient management of data in the cloud, especially with the ever-increasing volume of data, remains a significant challenge. Data deduplication is a crucial technique to reduce storage costs and enhance data management efficiency. It also concerns the secure cloud data storage and sharing. Here implement a novel approach to de duplication-based cloud data storage using chunk-based partitioning. In this approach, data is divided into fixed-size chunks, and a deduplication mechanism is applied to identify and eliminate redundant chunks. The process of duplicate detection leading to improved storage efficiency and reduced data redundancy. It not only conserves storage space but also enhances data retrieval performance by reducing the volume of data to be transferred over the network. Proposed system also enhance the security of cloud data storage by employing Base64 encoding for data representation and implementing re-encryption techniques based on the Advanced Encryption Standard (AES) for secure data sharing. The Base64 encoding methodology is employed to transform binary data into a text format, rendering it less susceptible to unintended manipulations during data transmission and storage. This encoding mechanism ensures data integrity and facilitates its secure storage in cloud environments. By utilizing AES encryption, data shared among users is re-encrypted with unique keys, ensuring that only authorized recipients can access and decrypt the shared information. The proposed approach significantly enhances data confidentiality and mitigates risks associated with unauthorized access, there by fortifying the security of cloud-based data sharing.

Secure Data Storage in Cloud Computing Using Code Based McEliece and NTRU Cryptosystems

Secure Data Storage in Cloud Computing Using Code Based McEliece and NTRU Cryptosystems

A Survey on Quantum-Cryptographic Image Encryption for Secure Storage

The aim of this paper is to explore and implement cutting-edge quantum cryptographic techniques to enhance the security of image data storage. Leveraging principles from quantum computing and cryptography, the project seeks to develop an advanced image encryption system that ensures unprecedented levels of security. By harnessing the unique properties of quantum mechanics, such as superposition and entanglement, the proposed system will establish a robust foundation for encrypting images, surpassing the limitations of classical cryptographic methods. The utilization of quantum key distribution protocols will add an additional layer of security, making it virtually impossible for adversaries to intercept or compromise sensitive image data during storage. This endeavor not only addresses the escalating cybersecurity concerns surrounding image storage but also contributes to the advancement of quantum technologies in practical applications. The project envisions a novel paradigm in image encryption, fostering a secure environment for sensitive visual information across various domains, including healthcare, finance, and defense. Keywords—Quantum Mechanics, Quantum Computing, cryptography, secure.

Securing synthetic faces: A GAN-blockchain approach to privacy-enhanced facial recognition

In recent years, facial recognition technology has become increasingly integrated into society, making privacy protection crucial. Previous techniques offered minimal secrecy safeguards through simple obscuration methods. This paper addresses the strict privacy requirements of face image data by developing a novel framework that synergistically integrates Generative Adversarial Networks (GANs), clustering algorithms, and Blockchain technology. The methodology proposes a cutting-edge Privacy-Preserving Self-Attention GAN (PPSA-GAN) to generate realistic synthetic facial imagery. An integrated mini-batch K-means clustering algorithm anonymizes these images into distinct groupings, maximizing privacy preservation. Blockchain integration complements the system by fortifying trust through decentralized ledgers for transparent yet secure data storage and auditing. Rigorous benchmarking on the CelebA dataset confirms the PPSA-GAN architecture’s state-of-the-art performance, attaining an impressive Inception Score of 13.99 and a Fréchet Inception Distance of 35.50. The mini-batch clustering forms 125 distinct clusters, effectively anonymizing facial attributes within the synthetic images. Blockchain integration further bolsters privacy assurances via tamper-proof historical records, showcasing precision, recall, F1-score, and accuracy values of 0.948, 0.938, 0.943, and 0.947, respectively. This multifunctional framework represents a novel contribution, fostering an ethical technological ecosystem that balances progress and privacy. Prospective deployment horizons encompass identity verification, surveillance infrastructure, and augmentation of medical image repositories, seeding an enlightening future for facial recognition domains.

A secure framework for effective workload resource management

An efficient and dynamic role-based access-control (RBAC) model is presented in this work which utilizes access-control for internet of things (IoT) nodes while minimizing storage and computational overhead. Also, for the identification of the malicious packets at the gateway server, a machine learning method has been presented. In addition, a framework for data management techniques in the IoT environment is designed to ensure efficient and secure storage, management, and processing of IoT data. The results have been evaluated by using the Montage and Cybershake workload in terms of energy consumption, processing time, detection accuracy and misclassification rate. The results show that the proposed secure framework for effective workload resource management (SFE-WRM) attains better performance in comparison to the reliable and energy‐efficient route selection (REERS) and FTA-WRM method. Also, by using the security method, the proposed method provides better security to the IoT nodes during the data aggregation and processing of the workload. The ultimate aim of this work is to provide a solution for the development of a secure and efficient IoT environment that can address critical security challenges and enable the widespread adoption of IoT devices and services.

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.

Cloud-Blockchain-Fusion–Based Secure Sharing Model for Air Traffic Management

Data sharing is the keystone to maintain the continuous operation of air traffic management (ATM). As situational awareness and collaborative decision-making become significant components of smart ATM, data sharing capability becomes a constraint on the development of ATM. The existing ATM data-sharing solutions have the following issues: 1) data-sharing efficiency is low; 2) data security has not received sufficient attention; and 3) access control is difficult to implement. Therefore, a security data-sharing model for ATM based on cloud-blockchain fusion is proposed, which realizes data security storage, intelligent service governance, and data authorized access. Firstly, a set of metafile storage structures is defined to realize the separate storage of data and metadata, which provides integrity verification and encrypted storage. Besides, using attribute-based encryption and service retrieval algorithms, the data publishing, discovery, and invocation functions based on smart contracts are realized. Finally, this scheme realizes secure data sharing in the ATM environment, and the computational cost and time cost are also kept at an acceptable level compared with other existing schemes.

Implementation of App Engine and Cloud Storage as REST API on Smart Farm Application

Smart Farm is an agricultural application that uses machine learning and cloud computing technology to improve efficiency in the farming process. Technological advancement and sustainable agriculture are two essential aspects of supporting global food security. This research investigates the implementation of App Engine and Cloud Storage in developing REST API in Smart Farm applications. By utilizing cloud computing technology, such as App Engine, and cloud storage, such as Cloud Storage, we can create efficient solutions to monitor and manage agriculture better. This research implements an App Engine and Cloud Storage to develop a REST API that allows Smart Farm application users to access data and control farming devices efficiently. The authors designed, developed, and tested this system to ensure optimal performance and reliability in agricultural data collection and distribution. This method has several significant advantages. First, App Engine allows for easy scalability, ensuring the system can handle increased data demand without disruption. Secondly, Cloud Storage provides secure and scalable storage for agricultural data, which can be accessed from anywhere. This provides easy and quick access to critical data for farmers. Moreover, the use of cloud technology also reduces infrastructure and maintenance costs. The developed system integrates the App Engine and Cloud Storage with the Smart Farm application. The App Engine is a processing engine that receives user requests via the REST API, processes the required data, and provides appropriate responses. Like image data, farm data is stored and managed on Cloud Storage. Users can access this data through the Smart Farm app or other devices, enabling better farming monitoring and decision-making.