Secure Platform Research Articles

A Secure and Transparent Voting System using Blockchain Technology

Blockchain-based voting systems present a transformative approach to resolving the limitations found in conventional voting methods. Traditional electoral processes frequently suffer from issues like vote manipulation, fraud, uncertainty, and inefficiencies in vote counting. By leveraging the decentralized nature of blockchain technology, these challenges can be mitigated, creating a secure, unchangeable, and transparent platform for vote recording and tallying. Once a vote is registered on the blockchain, it becomes unalterable, safeguarding election results against any form of tampering or external manipulation. Each vote is stored as a cryptographic entry, chained to the preceding data, which fortifies the overall security of the voting record against unauthorized modifications. This structure upholds the integrity of the election data, ensuring robust control over the electoral process. With the blockchain ledger publicly accessible, all participants—including voters and candidates—can verify results in real time, promoting confidence in the accuracy of the voting process. The technology's decentralized framework eliminates the need for centralized oversight, reducing the risks of manipulation or corruption by third parties. Additionally, this approach maintains voter privacy, allowing individuals to cast their votes confidentially without fear of personal information exposure or reprisal. Smart contracts within the system automate essential functions such as voter registration, identity validation, and vote casting, minimizing human errors and enabling seamless election processes. In conclusion, adopting blockchain technology in voting systems has the potential to greatly enhance security, transparency, and trust, thereby supporting and strengthening democratic practices on a global scale.

Securing the Chain: Comprehensive Analysis of Vulnerabilities and Defense Mechanisms in Blockchain Systems

Blockchain systems, characterized by decentralization, traceability, and robust security, are increasingly adopted across various domains including finance due to their strong disaster recovery capabilities. This paper focuses on the security mechanisms underpinning Bitcoin and Ethereum, two dominant cryptocurrencies, and examines potential vulnerabilities and attack vectors targeting blockchain technologies. Specifically, it addresses attacks on consensus mechanisms, network structures, and application layers, outlining the cryptographic methods integral to blockchain security. The study delves into the types of attacks that exploit these vulnerabilities, such as forking, stale or orphaned blocks, selfish mining, and majority attacks, which threaten the integrity and consistency of blockchain systems. Additionally, it reviews the current defense mechanisms and proposes new strategies to enhance the security and resilience of these digital platforms. By exploring these security challenges comprehensively, the paper aims to provide deeper insights into the defense strategies that can fortify blockchain against such vulnerabilities, thereby enhancing its reliability and trustworthiness in digital transactions and ensuring its sustainable integration into the digital economy.

Ethical considerations on the use of big Data and Artificial Intelligence in kidney research from the ERA ethics committee.

In the current paper, we will focus on requirements to ensure big data can advance the outcomes of our patients suffering from kidney disease. The associated ethical question is whether and how we as a nephrology community can and should encourage the collection of big data of our patients. We identify some ethical reflections on the use of big data, and their importance and relevance. Furthermore, we balance advantages and pitfalls and discuss requirements to make legitimate and ethical use of big data possible. The collection, organization and curation of data come upfront in the pipeline prior to any analyses. Great care must therefore be taken to ensure quality of the data at this stage, to avoid the garbage in garbage out problem and suboptimal patient care as a consequence of such analyses. Access to the data should be organized so that correct and efficient use of data is possible. This means that data must be stored safely, so that only those entitled to do so can access them. At the same time, those who are entitled to access the data should be able to do so in an efficient way, so as not to hinder relevant research. Analysis of observational data is itself prone to many errors and biases. Each of these biases can finally result in provision of low-quality medical care. Secure platforms should therefore also ensure correct methodology is used to interpret the available data. This requires close collaboration of a skilled workforce of experts in medical research and data scientists. Only then will our patients be able to benefit fully from the potential of AI and big data.

Innovative model for security of multi-cloud platform: data integrity perspective

Innovative model for security of multi-cloud platform: data integrity perspective

Virtual discharge counseling: An assessment of scalability of a novel patient educational process across a multi-site urban emergency department.

Inadequate counseling at patient discharge from the emergency department can lead to adverse patient outcomes. Virtual discharge counseling can address gaps in discharge counseling and improve patients' understanding of instructions. A previously established virtual discharge counseling program was scaled across three emergency departments and expanded to 13 diagnoses. Utilizing a standardized protocol and script, counselors performed virtual discharge counseling via a remote, secure teleconference platform in the patients' preferred language. Virtual discharge counseling was performed with 166 patients. COVID-19, back pain, and headache were the most frequent diagnoses. The median counseling time was 14 min. Median counseling time for English was 11 min, versus 20 min for other languages (p < 0.001). Counseling times were the longest for COVID-19 and diabetes (18 min for each). We demonstrate the scalability of a virtual discharge counseling program. Our findings can assist in targeting virtual discharge counseling resources for limited English-proficiency patients and specific diagnoses that require longer counseling times.

From Grapes to Clicks : A Cloud-Based Wine-Selling Solution for Single Vendors

This document outlines the design and creation of a robust wine vending application for a single vendor, which combines a web platform, an Android mobile app, and a cloud-based framework. This solution addresses the increasing demand for online wine sales while delivering a smooth, secure, and adaptable shopping experience. Essential features of the system include an intuitive web interface and an application focused on effective product searching, ordering, and payment processing. Cloud infrastructure is implemented to guarantee high availability, scalability, and secure data management. The paper discusses various architectural alternatives, such as utilizing cloud services for load balancing, data handling, and disaster recovery. It also emphasizes the necessity of strong security protocols, including encryption, user authentication, and adherence to industry standards for online transactions. Additionally, the document addresses challenges related to regulatory compliance, including age verification mandates and data protection laws. Lastly, it explores strategies for performance enhancement, improvements to user experience, and potential avenues for future expansion. The proposed solution aims to deliver a dependable, secure, and scalable platform tailored to the requirements of individual wine retailers while accommodating future growth.

IDENTIFICATION AND LOCALIZATION OF VULNERABILITIES IN SMART CONTRACTS USING ATTENTION VECTORS ANALYSIS IN A BERT-BASED MODEL

Context. With the development of blockchain technology and the increasing use of smart contracts, which are automatically executed in blockchain networks, the significance of securing these contracts has become extremely relevant. Traditional code auditing methods often prove ineffective in identifying complex vulnerabilities, which can lead to significant financial losses. For example, the reentrancy vulnerability that led to the DAO attack in 2016 resulted in the loss of 3.6 million ethers and the split of the Ethereum blockchain network. This underscores the necessity for early detection of vulnerabilities. Objective. The objective of this work is to develop and test an innovative approach for identifying and localizing vulnerabilities in smart contracts based on the analysis of attention vectors in a model using BERT architecture. Method. The methodology described includes data preparation and training a transformer-based model for analyzing smart contract code. The proposed attention vector analysis method allows for the precise identification of vulnerable code segments. The use of the CodeBERT model significantly improves the accuracy of vulnerability identification compared to traditional methods. Specifically, three types of vulnerabilities are considered: reentrancy, timestamp dependence, and tx.origin vulnerability. The data is preprocessed, which includes the standardization of variables and the simplification of functions. Results. The developed model demonstrated a high F-score of 95.51%, which significantly exceeds the results of contemporary approaches, such as the BGRU-ATT model with an F-score of 91.41%. The accuracy of the method in the task of localizing reentrancy vulnerabilities was 82%. Conclusions. The experiments conducted confirmed the effectiveness of the proposed solution. Prospects for further research include the integration of more advanced deep learning models, such as GPT-4 or T5, to improve the accuracy and reliability of vulnerability detection, as well as expanding the dataset to cover other smart contract languages, such as Vyper or LLL, to enhance the applicability and efficiency of the model across various blockchain platforms. Thus, the developed CodeBERT-based model demonstrates high results in detecting and localizing vulnerabilities in smart contracts, which opens new opportunities for research in the field of blockchain platform security.

Exploring Knowledge of Aging in Speech-Language Pathologists: A Follow-Up Empirical Study

Purpose: Almost 40% of certified speech-language pathologists (SLPs) are employed in health care facilities where the percentage of aging patients is increasing. Historical research indicates a high level of implicit bias surrounding aging within the profession. The purpose of this study was to examine the current state of knowledge and implicit bias among medically based SLPs. Method: An online survey was distributed using a secure platform, and convenience sampling was conducted. Medical SLPs ( N = 70) completed the Facts on Aging Quiz and questions detailing practice settings, experience, and other demographic information. Results: Data were analyzed using descriptive statistics with confidence intervals and segregated into demographic groups. Overall, the scores indicated less than optimal knowledge and implicit bias among the participants, with an M score of 67. Median and mode scores were 68. Analysis of variance between the groups showed no significant differences in ethnicity, work setting, age, experience, education, or percent of aging patients on caseload. Conclusions: Compared with seminal research, little has changed in 20 years. This investigation, although relatively small in sample size, highlights the need for increased education and focuses on aging populations.

RESPONSE OF LIQUID MANURES AS ALTERNATIVE NUTRIENT ON GROWTH AND YIELD OF TOMATO

This research explores the transformative role of advanced technologies, particularly AI, machine learning (ML) and block chain, in the development of smart e-commerce solutions that enhance user experiences and operational efficiencies. It also explores how customized algorithms can improve customer engagement, boost conversion rates and encourage sustainable shopping practices. Additionally, the research investigates AI-driven solutions to encourage sustainable shopping behaviors and optimize shopping post-purchase experiences. Specifically, it aims to assess the impact of AI-based personalization algorithms, examine the trade-offs between data privacy and user experience and analyze AI's influence on consumer trust and decision-making processes. Primary data from surveys and interviews are supplemented with secondary data on customer behavior and sales metrics to evaluate the effectiveness of technologies like AI, machine learning and block chain in driving personalized, ethical and efficient e-commerce. A mixed-method research design, combining qualitative and quantitative approaches, employed to capture insights from key stakeholders (e.g., consumers, developers and e-commerce managers) and assess the impact of smart e-commerce platforms on shopping behavior, trust and loyalty. Implementing the Agile Model, the research iteratively addresses system requirements, testing and deployment challenges to create a flexible, scalable solution. Key findings underscore the role of predictive modeling in inventory management, the ethical potential of sustainable product recommendations and the positive consumer responses to transparency initiatives. The study contributes valuable insights into the development of secure, personalized and ethical e-commerce platforms that align with modern consumer expectations for convenience and responsibility.

Comparative Analysis of Healthcare Cost between M-DPS vs. DPS Architecture by Using Blockchain Technology

The potential for blockchain technology to transform healthcare systems globally is enormous. Blockchain technology has the potential to revolutionize healthcare by creating a decentralized, transparent, and secure platform that can tackle issues like data interoperability, supply chain management, and patient participation. Using blockchain technology, this article compares and contrasts the healthcare expenses of two architectures: The Modified Decentralized Patient System (MPS) and the Decentralized Patient System (DPS). The study examines the gas costs linked with transactions to assess the economic impact of implementing these designs in healthcare settings. The paper compares the M-DPS and DPS architectures and determines the gas costs associated with file uploads. Furthermore, the article compares the expenses of file uploads on blockchain networks that use IPFS (InterPlanetary File System) and those that do not. Results shed light on possible monetary gains and losses from using blockchain technology in healthcare data management systems.

UC CCS CAMPUSCORE: A Secure Digital Repository as Knowledge Management System

The UC-CCS CampusCore is a secure and centralized digital repository designed to address the challenges faced by students and faculty members in managing their course and research deliverables at the University of Cebu - Main Campus. This study aims to validate the problems faced by students and faculty members and assess the feasibility of the proposed solution. The research methodology involves problem and solution validation through surveys and interviews. The results indicate a high level of favorability for the proposed system among both students and faculty members, emphasizing the need for a secure and accessible platform. The findings will inform the development of the UC-CCS CampusCore system, ensuring its alignment with user needs and requirements.

Decision Support Model for Selecting the Optimal Blockchain Oracle Platform: An Evaluation of Key Factors

Smart contract-based applications are executed in a blockchain environment, and they cannot directly access data from external systems, which is required for the service provision of these applications. Instead, smart contracts use agents known as blockchain oracles to collect and provide data feeds to the contracts. The functionality and compatibility with smart contract applications need to be considered when selecting the best-fit oracle platform. As the number of oracle alternatives and their features increases, the decision-making process becomes increasingly complex. Selecting the wrong or sub-optimal oracle is costly and may lead to severe security risks. This paper provides a decision support model for the oracle selection problem. The model supports smart contract decision-makers in selecting a secure, cost-effective, and feasible oracle platform for their applications. We interviewed oracle co-founders and smart contracts experts to refine and validate the decision model. Two real-world smart contract application case studies were used to evaluate the model. Our model prioritises and suggests more than one possible oracle platform based on the developer's required criteria, security assessment, and cost analysis. Moreover, this guided decision model serves to reveal issues that may go unnoticed if done haphazardly, reduce decision-making efforts, and provide a cost-effective solution.

LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT Plugins

Large language model (LLM) platforms, such as ChatGPT, have recently begun offering an app ecosystem to interface with third-party services on the internet. While these apps extend the capabilities of LLM platforms, they are developed by arbitrary third parties and thus cannot be implicitly trusted. Apps also interface with LLM platforms and users using natural language, which can have imprecise interpretations. In this paper, we propose a framework that lays a foundation for LLM platform designers to analyze and improve the security, privacy, and safety of current and future third-party integrated LLM platforms. Our framework is a formulation of an attack taxonomy that is developed by iteratively exploring how LLM platform stakeholders could leverage their capabilities and responsibilities to mount attacks against each other. As part of our iterative process, we apply our framework in the context of OpenAI's plugin (apps) ecosystem. We uncover plugins that concretely demonstrate the potential for the types of issues that we outline in our attack taxonomy. We conclude by discussing novel challenges and by providing recommendations to improve the security, privacy, and safety of present and future LLM-based computing platforms. The full version of this paper is available online at https://arxiv.org/abs/2309.10254

A conceptual hybrid model based on Ethereum for implementing sustainable development goals

The United Nations’ sustainable development goals (SDGs) present a global challenge that demands innovative solutions. Combining emerging technologies can be a transformative approach to achieving these goals and overcoming associated challenges. Blockchain provides a secure and transparent platform, while big data and data mining enable the analysis of vast datasets to identify critical areas and measure progress. Additionally, blockchain facilitates secure knowledge exchange among stakeholders through knowledge sharing and management. Recent research demonstrates the effectiveness of blockchain in implementing SDGs. Our study explores the benefits of various technologies and proposes a hybrid model based on Ethereum. This model leverages additional technologies to enhance performance, increase transparency, and reduce gas fees. This combination empowers informed decision-making and accelerates progress towards a sustainable future.

A Framework for Enhancing Maintainability of CMS in Kenyan Universities' LMS

The maintainability of Content Management Systems (CMS) within Learning Management Systems (LMS) is critical in educational institutions to ensure efficient content management, system scalability, and security. This paper presents a framework aimed at enhancing the maintainability of CMS within LMS in Kenyan universities. The research utilizes Cognitive Load Theory and Software Evaluation Theory to address CMS challenges, focusing on perfective, corrective, adaptive, and preventive maintenance. This paper also discusses key factors influencing CMS maintainability, including system documentation, modularity, and error handling. Findings from this study contribute to the development of a framework and advancing CMS design in LMS, providing a robust, scalable, and secure platform for educators and administrators.

NetSentinel applications for website security

NetSentinel is an extensive web security platform designed to scan, detect and protect websites from potential vulnerabilities and cyber-threats. NetSentinel offers a proactive approach to strengthen and safeguard the website’s security, in an era where data breaches and cyber-attacks are common. The platform analyzes critical security features like SSL security validity, DNSSEC implementation, threat detection, security headers, firewall, and open ports. The main feature of the platform is the scoring mechanism, which assesses the website’s overall security to ensure it is protected from potential threats. The scoring method assigns different weights to the various security features according to their importance in the calculation. This scoring system helps users focus on the most important vulnerabilities and gives clear, practical advice on how to mitigate risks. Along with the main scoring feature the platform also includes 24 other critical security features which are Server location, Whois, DNS Data, DNS Records, Archives, Carbon Footprint, TLS Cipher Suites, TLS Security Configuration, TLS Handshake Simulation, Status, Traceroute, Blocklists, Pages, Social Tags, Headers, HSTS Check, Domain Rank, Tranco Rank, Linked Pages, Mail Services, Redirects, Security Txt, Robots Txt. NetSentinel breaks down complex information into a simple, easy to understand security score, which makes security assessment easier. It also offers clear explanations of the issues and provides step-by-step recommendations on how to fix them, helping users improve their overall security without needing advanced technical knowledge to be possessed. Using this platform, organizations can enhance their site’s ability to withstand cyber threats, ensuring data integrity and secure communication. NetSentinel helps users take control of their web security by balancing technical accuracy with easy-to-use interfaces, making it simple for everyone to stay proactive about their safety online.

Quantitative Finance and Information Technologies: A Comparative Analysis of Quantitative Trading and Cryptocurrency and Their Regulatory Challenges

As technology has improved in the last decade, financial institutions have developed new technologies, including quantitative trading and cryptocurrency, to enhance their financial products and services. This paper first provides a brief background of quantitative trading and argues for the transactional efficiency of quantitative trading over traditional trading practices; it characterizes quantitative trading as fast and precise. Meanwhile, the study also accounts for the regulatory concerns–including data leakage and platform security–that quantitative trading firms may encounter. This study then establishes a distinction between cryptocurrency and quantitative trading–the former is money-driven, and the latter is data-driven. This paper then discusses the speculative nature of cryptocurrency and addresses its financial concerns citing the FTX collapse. Overall, this paper establishes the argument that quantitative trading supported by technological experts and facilitators offers more advantages than disadvantages compared to cryptocurrency trading. This research concludes that since quantitative trading and cryptocurrency trading are conducted without consideration for international boundaries, they offer bold financial potential as alternatives to traditional banking practices, as long as specific international financial laws are complied with.

Reprogrammable metasurface holographic image encryption technology based on a three-dimensional discrete hyperchaotic system.

The emergence of metasurfaces provides a secure and efficient platform for optical encryption technology as they have broad prospects in the field of information security. However, the limited number of channels available on metasurfaces and the insufficient security of keys make them vulnerable to attacks by eavesdroppers. In this work, a reprogrammable metasurface optical encryption scheme based on a three-dimensional hyperchaotic system is proposed. The three-dimensional discrete hyperchaotic system has strong ergodicity, initial value sensitivity, and pseudorandomness compared to previous chaotic systems that can pass NIST randomness testing well. Additionally, based on this hyperchaotic property, we designed a metasurface encryption structure based on the geometric phase. The research results show that the introduction of the hyperchaotic system greatly improves the randomness and flexibility of key generation. This scheme can encrypt multiple images with high security. Decryption is only possible when the attacker steals the complete chaotic system and parameters, as well as over 70% of the correct incident light phase information. Our research results have great potential applications in the field of metasurface optical encryption.

Neurologists' Attitudes and Perceptions on Palliative Care: A Qualitative Study.

Despite significant advances in the treatment of neurologic disorders, many conditions require complex care planning and advanced care planning. Neurologists are in a unique position because they are integral in providing patient centered care, understanding neurologic disease and illness trajectory, and how disease can affect patients' sense of self and values. Currently, little is known about neurologists' perceptions and challenges in care planning and palliative care for their patients. Neurologists from one Canadian academic institution participated in a 30-minute semistructured interview from November 2022 to April 2023. Interviews were conducted until saturation was reached and confirmed. Interviews occurred online through a secure platform or in-person and were recorded. Data were analyzed using a constant comparative method using constructivist grounded theory. Member checking was conducted post interview. Ten neurologists participated across a broad spectrum of neurology experience and subspecialties. We developed a detailed theory of understanding neurologists' attitudes and perceptions of palliative care. When neurologists delay or fail to initiate care planning discussions or palliative care, it results from a complex interplay between patient, physician, and resource accessibility factors. Certain contextual factors, such as a first visit or follow-up, inpatient vs outpatient setting, clinic culture, and the type of clinic practice, are factors that can influence these conversations. As a result, physicians may fail to use available resources, or they may involve other care providers or refer to subspecialty neurologic clinics. However, this delay can still lead to patient and provider harm. Opportunities to improve care exist with continuing education opportunities for trainees and staff, collaboration with palliative care specialists, and health systems support, such as increasing public awareness to address misconceptions about palliative care and resource availability. Our findings identify that failure or delay to initiate care planning and palliative care by neurologists results from a complex interplay between local culture, experience, context, practice type, and patient factors. Opportunities to improve care include increasing educational opportunities, building integrated and collaborative practices, and dedicated health systems support.

Causal evidence for social group sizes from Wikipedia editing data.

Human communities have self-organizing properties in which specific Dunbar Numbers may be invoked to explain group attachments. By analysing Wikipedia editing histories across a wide range of subject pages, we show that there is an emergent coherence in the size of transient groups formed to edit the content of subject texts, with two peaks averaging at around for the size corresponding to maximal contention, and at around as a regular team. These values are consistent with the observed sizes of conversational groups, as well as the hierarchical structuring of Dunbar graphs. We use a model of bipartite trust to derive a scaling law that fits the data and may apply to all group size distributions when these are based on attraction to a seeded group process. In addition to providing further evidence that even spontaneous communities of strangers are self-organizing, the results have important implications for the governance of the Wikipedia commons and for the security of all online social platforms and associations.