Data security is, protecting data from ill- conceived get to, utilize, introduction, intrusion, change, examination, recording or destruction. Cloud computing is a sort of Internet-based computing that grants conjoint PC handling resources and information to PCs what's more, different gadgets according to necessity. It is a model that empowers universal, on-request access to a mutual pool of configurable computing resources. At present, security has been viewed as one of the best issues in the improvement of Cloud (...) Computing. The key issue in effective execution of Cloud Computing is to adequately deal with the security in the cloud applications. This paper talks about the part of cryptography in cloud computing to improve the data security. The expectation here is to get bits of knowledge another security approach with the usage of cryptography to secure information at cloud data centers. (shrink)

This research presents a novel and efficient public key cryptosystem known as the Enhanced Schmidt Samoa (ESS) cryptosystem, proposed to safeguard the data of a single owner in cloud computing environments. Data storage is a one-time process in the cloud, while data retrieval is a frequent operation. Experimental results demonstrate that the ESS cryptosystem offers robust data confidentiality in the cloud, surpassing the security provided by traditional cryptosystems. The research also introduces a secure cloud framework (...) designed to accommodate both individuals and organizations accessing applications and data in the cloud. While individual users may generate and share data, organizations often involve multiple users in data sharing to support their business processes. In these scenarios, multi-user data ownership and access management become critical, requiring secure sharing of cryptographic keys among the authorized users. To address these issues and ensure data confidentiality in multi-user cloud environments, the Improved Secure Cloud Data Storage Framework (ISCDSF) is introduced. This research not only enhances data security but also provides a comprehensive framework for secure data sharing in the cloud, catering to the needs of both individual users and organizations. (shrink)

Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments (...) are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different (...) machine learning algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different (...) machine learning algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple (...) cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. Future enhancements may include integrating machine learning algorithms to predict and adapt to new security threats in real time, further solidifying the reliability of cloud storage solutions. (shrink)

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising (...) the integrity and confidentiality of the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. Furthermore, the implementation of an optimized keyword indexing mechanism facilitates fast and accurate search operations, making the protocol well-suited for real-time IoT applications. Extensive experiments were conducted using various IoT datasets to evaluate the performance of the proposed protocol in terms of encryption speed, search efficiency, and overall security. The results demonstrate that the optimized encryption protocol significantly outperforms existing methods, offering a scalable solution that meets the stringent requirements of IoT systems. This research contributes to the field by providing a practical and secure encryption solution that addresses the unique challenges of IoT environments, paving the way for more secure and efficient IoT networks. (shrink)

Cloud computing is the computing technology which provides resources like software, hardware, services over the internet. Cloud computing provides computation, software, data access, and storage services that do not require end- user knowledge of the physical location and configuration of the system that delivers the services. Cloud computing enables the user and organizations to store their data remotely and enjoy good quality applications on the demand without having any burden associated with local hardware resources and software managements but (...) it possesses a new security risk towards correctness of data stored at cloud. The data storage in the cloud has been a promising issue in these days. This is due to the fact that the users are storing their valuable data and information in the cloud. The users should trust the cloud service providers to provide security for their data. Cloud storage services avoid the cost storage services avoids the cost expensive on software, personnel maintains and provides better performance less storage cost and scalability, cloud services through internet which increase their exposure to storage security vulnerabilities however security is one of the major drawbacks that preventing large organizations to enter into cloud computing environment. This work surveyed on several storage techniques and this advantage and its drawbacks. (shrink)

Cloud computing has transformed data management and storage by providing unmatched scalability, flexibility, and cost-effectiveness. However, rising cloud storage use has raised data security and privacy issues. As sensitive data being outsourced to third-party cloud providers, security is crucial. Traditional encryption methods secure data but make data recovery difficult. Specifically, efficiently searching encrypted data without compromising security is difficult.

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple (...) cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine (...) learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random (...) Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (shrink)

This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized (...) ABKS not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data (...) security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. (shrink)

This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes of data, (...) resource management complexities in optimizing computational resources, cost management strategies to balance performance with expenses, and ensuring robust data security and privacy measures. The manuscript then delves into innovative solutions and techniques aimed at addressing these challenges. It discusses advancements such as workflow automation tools and frameworks that streamline repetitive tasks, containerization technologies like Docker and Kubernetes for efficient application deployment and management, and the utilization of serverless architectures to enhance scalability and reduce operational costs. Additionally, it explores the benefits of parallel processing frameworks such as Apache Spark and Hadoop in optimizing data processing tasks. The integration of machine learning algorithms for dynamic workflow optimization and effective data management strategies in cloud environments are also examined. Through detailed case studies and application examples across various domains, the manuscript illustrates the practical implementation and outcomes of these optimization strategies. Furthermore, it discusses emerging trends in cloud technologies, the role of AI-driven automation in enhancing workflow efficiencies, and ethical considerations surrounding data science operations in cloud computing. The manuscript concludes with a summary of findings, practical recommendations for organizations seeking to enhance their data science workflows in the cloud, and insights into future research directions to address evolving challenges. (shrink)

This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms (...) for predictive search improvements a. (shrink)

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword searches over encrypted (...) data. This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized ABKS not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data (...) security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for predictive search improvements and strengthening ABKS against emerging security vulnerabilities are future research priorities. (shrink)

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword searches over encrypted (...) data. (shrink)

The first few years of the 21st century were characterised by a progressive loss of privacy. Two phenomena converged to give rise to the data economy: the realisation that data trails from users interacting with technology could be used to develop personalised advertising, and a concern for security that led authorities to use such personal data for the purposes of intelligence and policing. In contrast to the early days of the data economy and internet surveillance, (...) the last few years have witnessed a rising concern for privacy. As bad data practices have come to light, citizens are starting to understand the real cost of using online digital technologies. Two events stamped 2018 as a landmark year for privacy: the Cambridge Analytica scandal, and the implementation of the European Union’s General Data Protection Regulation (GDPR). The former showed the extent to which personal data has been shared without data subjects’ knowledge and consent and many times for unacceptable purposes, such as swaying elections. The latter inaugurated the beginning of robust data protection regulation in the digital age. Getting privacy right is one of the biggest challenges of this new decade of the 21st century. The past year has shown that there is still much work to be done on privacy to tame the darkest aspects of the data economy. As data scandals continue to emerge, questions abound as to how to interpret and enforce regulation, how to design new and better laws, how to complement regulation with better ethics, and how to find technical solutions to data problems. The aim of the research project Data, Privacy, and the Individual is to contribute to a better understanding of the ethics of privacy and of differential privacy. The outcomes of the project are seven research papers on privacy, a survey, and this final report, which summarises each research paper, and goes on to offer a set of reflections and recommendations to implement best practices regarding privacy. (shrink)

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising (...) the integrity and confidentiality of the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. Furthermore, the implementation of an optimized keyword indexing mechanism facilitates fast and accurate search operations, making the protocol well-suited for real-time IoT applications. Extensive experiments were conducted using various IoT datasets to evaluate the performance of the proposed protocol in terms of encryption speed, search efficiency, and overall security. The results demonstrate that the optimized encryption protocol significantly outperforms existing methods, offering a scalable solution that meets the stringent requirements of IoT systems. This research contributes to the field by providing a practical and secure encryption solution that addresses the unique challenges of IoT environments, paving the way for more secure and efficient IoT networks. (shrink)

Data mining possess a significant threat to ethics, privacy, and legality, especially when we consider the fact that data mining makes it difficult for an individual or consumer (in the case of a company) to control accessibility and usage of his data. Individuals should be able to control how his/ her data in the data warehouse is being access and utilize while at the same time providing enabling environment which enforces legality, privacy and ethicality on (...) data scientists, or data engineer during data mining process. This paper review issues of legality, privacy, and ethicality in data mining, review processes of Data mining, and also proposes solution to current ethical and privacy issue in data mining. It introduces a new method which enforces data mining without infringing on the privacy of individual or consumer whose data are being used. The sole aim of this paper is to propose a new method of mining data which restricts scientists within the constraints of legality, privacy, and ethicality. (shrink)

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the (...) audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas. (shrink)

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular (...) focus on developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy. (shrink)

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We (...) also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities. (shrink)

ICTs, personal data, digital rights, the GDPR, data privacy, online security… these terms, and the concepts behind them, are increasingly common in our lives. Some of us may be familiar with them, but others are less aware of the growing role of ICTs and data in our lives - and the potential risks this creates. These risks are even more pronounced for vulnerable groups in society. People can be vulnerable in different, often overlapping, ways, which place (...) them at a disadvantage to the majority of citizens; Table 3 in this guide presents some of the many forms and causes of vulnerability. As a result, vulnerable people need greater support to navigate the digital world, and to ensure that they are able to exercise their rights. This guide explains where such support can be found, and also answers the following questions: - What are the main ethical and legal issues around ICTs for vulnerable citizens? - Who is vulnerable in Europe? - How do issues around ICTs affect vulnerable people in particular? This guide is a resource for members of vulnerable groups, people who work with vulnerable groups, and citizens more broadly. It is also useful for data controllers1 who collect data about vulnerable citizens. While focused on citizens in Europe, it may be of interest to people in other parts of the world. It forms part of the Citizens’ Information Pack produced by the PANELFIT project, and is available in English, French, German, Italian and Spanish. You are welcome to translate this guide into other languages. Please send us a link to online versions in other languages, so that we can add them to the project website. (shrink)

The dishonest practices in the conduct of educational research in South-South region particularly, and Nigeria generally, has called for urgent intervention. This study assessed data management practices and educational research effectiveness of University Lecturers in South-South Nigeria. A sample of 602 lecturers that were accessible during data collection responded to two questionnaires (Data Management Practices Questionnaire – DMPQ and "Educational Research Effectiveness Questionnaire – EREQ). Multiple regression was employed in the analysis of data. Findings revealed amongst (...) others that; data storage, data security, data retrieval, data sharing, and data re-use jointly contributed to the total variance in educational research effectiveness in terms of proper citations by 56.25%, problem-solving by 22.14%, knowledge creation by 34.50%, and generation of testable data by 36.88%; and their composite influence on educational research effectiveness in terms of proper citations (F=152.25,p<.05), problem-solving (F=33.90,p<.05), knowledge creation (F=62.78,p<.05), and generation of testable data (F=69.65,p<.05), is statistically significant. Based on the findings of this study, it was recommended among others that, there should be an up-to-date database or data warehouse in Ministries of Education where researchers can easily access data in order to increase the quality of researches by eliminating the problems of data fabrication and falsification. This study contributes to the existing body of knowledge by addressing the gaps arising from the paucity of research literature on educational research effectiveness. This study shows how data management practices affects the quality of educational research output of university lecturers using empirical evidence from Nigeria. (shrink)

Purpose: The research explored empirical evidence to assess the impact of cyber security and supply chain risk on digital operations in the UAE pharmaceutical industry. Methodology/Design/Approach: Based on responses from 243 personnel working at 14 pharmaceutical manufacturing companies in Dubai, data were examined for normality, instrument validity and regression analysis. Cyber security and SC risk on digital operations were explored by applying convenient sampling and descriptive and analytical research design. Findings: The findings validated the significant positive association (...) between cyber security and supply chain risk with digital operations. Research implications and Limitations: The research model was developed with three variables evaluated only on the pharmaceutical industry in Dubai. Future research should focus on multiple manufacturing industries by covering a larger geographic area. Practical implications: When suppliers are highly concentrated, customer-focused organisations with uncertain levels of digital transformation could improve their ability to manage supply chain risk by diversifying their clients. Pharmaceutical firms may require a greater focus on technology-based manufacturing firms to build and maintain customer trust. Originality Value: To illustrate how the pharmaceutical industry has explored the relationship between cyber security, supply chain risk and digital operations, the research highlights the significant and convoluted consequences of the relationship model that have not been previously considered. (shrink)

In recent years, the growing academic field called “Data Science” has made many promises. On closer inspection, relatively few of these promises have come to fruition. A critique of Data Science from the phenomenological tradition can take many forms. This paper addresses the promise of “participation” in Data Science, taking inspiration from Paul Majkut’s 2000 work in Glimpse, “Empathy’s Impostor: Interactivity and Intersubjectivity,” and some insights from Heidegger’s "The Question Concerning Technology." The description of Data Science (...) provided in the scholarly literature includes “the study of the generalizable extraction of knowledge from data” (Dhar 2013, 64), “data stewardship and data sharing…access to data at higher volumes and more quickly, and the potential for replication and augmentation of existing research” (Hartter et al., 2013, 1), and “personal information, health status, daily activities and shopping preferences that are recorded and used to give us instant feedback and recommendations based on previous online behavior.” (Shin 2013) United States universities have begun to offer graduate programs in “data science”, anticipating the growth of this field for marketing, national security, and health industries. These universities include New York University, Columbia University, Stanford, Northwestern, and Syracuse. (shrink)

E-Government refers to the administration of Information and Communication Technologies (ICT) to the procedures and functions of the government with the objective of enhancing the transparency, efficiency and participation of the citizens. E-Government is tough systems that require distribution, protection of privacy and security and collapse of these could result in social and economic costs on a large scale. Many of the available e-government systems like electronic identity system of management (eIDs), websites are established at duplicated databases and servers. (...) An established validation and management system could face a single failure point and the system is prone to Distributed Denial of Service Attacks (DDoS), denial of service attacks (DoS), malware and other cyber attacks. The execution of a privacy preserving and a secure decentralized system is enabled by the block chain technology. Here any third-party organizations do not have any control over the transactions of the Government. With the help of block chain technology, new and existing data are encapsulated within ledger or blocks, which are evenly distributed through the network in an enduring and sustainable way. The privacy and security of information are improved with the help of block chain technology, where distribution and encryption of data are performed through the total network. This analytical paper maps out the analysis of the security in the e-government system, utilizing the block chain technology that provides privacy and security of information and thereby enhancing the trust among the public sector. Qualitative and theoretical analysis is made for the proposed topic and implications of privacy and security of the proposed system is made. (shrink)

Background: Use of patients’ medical data for secondary purposes such as health research, audit, and service planning is well established in the UK. However, the governance environment, as well as public understanding about this work, have lagged behind. We aimed to systematically review the literature on UK and Irish public views of patient data used in research, critically analysing such views though an established biomedical ethics framework, to draw out potential strategies for future good practice guidance and inform (...) ethical and privacy debates. Methods: We searched three databases using terms such as patient, public, opinion, and electronic health records. Empirical studies were eligible for inclusion if they surveyed healthcare users, patients or the public in UK and Ireland and examined attitudes, opinions or beliefs about the use of patient data for medical research. Results were synthesised into broad themes using a framework analysis. Results: Out of 13,492 papers and reports screened, 20 papers or reports were eligible. While there was a widespread willingness to share patient data for research for the common good, this very rarely led to unqualified support. The public expressed two generalised concerns about the potential risks to their privacy. The first of these concerns related to a party’s competence in keeping data secure, while the second was associated with the motivation a party might have to use the data. Conclusions: The public evaluates trustworthiness of research organisations by assessing their competence in data-handling and motivation for accessing the data. Public attitudes around data-sharing exemplified several principles which are also widely accepted in biomedical ethics. This provides a framework for understanding public attitudes, which should be considered in the development in any guidance for regulators and data custodians. We propose four salient questions which decision makers should address when evaluating proposals for the secondary use of data. (shrink)

Researchers and practitioners are increasingly using machine‐generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while—potentially—mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of (...) synthetic data is often understood to be its detachment from the data subjects whose measurement data is used to generate it. However, we argue that addressing the ethical issues synthetic data raises might require bringing data subjects back into the picture, finding ways that researchers and data subjects can be more meaningfully engaged in the construction and evaluation of datasets and in the creation of institutional safeguards that promote responsible use. (shrink)

This article argues that personal medical data should be made available for scientific research, by enabling and encouraging individuals to donate their medical records once deceased, similar to the way in which they can already donate organs or bodies. This research is part of a project on posthumous medical data donation developed by the Digital Ethics Lab at the Oxford Internet Institute at the University of Oxford. Ten arguments are provided to support the need to foster posthumous medical (...) data donation. Two major risks are also identified—harm to others, and lack of control over the use of data—which could follow from unregulated donation of medical data. The argument that record-based medical research should proceed without the need to secure informed consent is rejected, and instead a voluntary and participatory approach to using personal medical data should be followed. The analysis concludes by stressing the need to develop an ethical code for data donation to minimise the risks, and offers five foundational principles for ethical medical data donation suggested as a draft code. (shrink)

In light of Taiwan's extensive exposure to the Chinese Communist Party's "cognitive domain infiltration warfare," this paper proposes new response mechanisms and strategies for cybersecurity and national defense. The focus is primarily on assessing the CCP's cognitive infiltration tactics to develop policy recommendations in cybersecurity linguistics. These recommendations are intended to serve as a reference for future national defense and information security policies. Within the constraints of limited resources, this study attempts to provide an integrated analysis method combining qualitative (...) and quantitative tools. This method involves text mining in cybersecurity linguistics, focusing on "textual content" and incorporating discourse deconstruction. The aim is to enhance the success rate in identifying and defining contentious information in cognitive warfare, thereby offering policy suggestions. -/- Keywords: Cognitive Infiltration, Big Data Corpus, Cybersecurity Linguistics, Text Mining Analysis, Narrative Language Deconstruction. (shrink)

The security problems of Nigeria have continued to stare at her very ominously and intermittently harass her, both within and outside her shores. These have lingered on and have created a clog on the wheel of the country's progress, indeed dramatically stagnating, and to say the least, truncating the mainstay of the country's survival. Several interpretations, theories, analyses, syntheses, and jingoistic conceptualization have been propagated, all producing the same result. From scientific to technological approach, religious to ritualist approach, political (...) to social approach, the security situation rather than improving, is static and under some regimes retarded. Academic contributions, especially ideas from the humanities and indeed the discipline of history are jettisoned, in fact, quickly dusted into the waste bin. The concern of this paper is to attempt a historical investigation of the security problems in Nigeria, identifying them to show how information and communication technology can help in curbing these challenges, using the realist paradigm as a theoretical framework. This study identifies Nigerians as the cause of her security problems, who rather than face these seismic challenges head-on, abandon them – a cowardly act, ending in futility and deeper chaos. This study adopts both primary and secondary sources of data collection. It is hoped that if academic exercises are not mere, this input may create a turnaround in the security situation in Nigeria. (shrink)

The future of health research will be characterised by three continuing trends: rising demand for health data; increasing impracticability of obtaining specific consent for secondary research; and decreasing capacity to effectively anonymise data. In this context, governments, clinicians and the research community must demonstrate that they can be responsible stewards of health data. IRBs and RECs sit at heart of this process because in many jurisdictions they have the capacity to grant consent waivers when research is judged (...) to be of particular value. However, several different terms are used to refer to this value, indicating a lack of conceptual clarity regarding the appropriate test for access to health data for research without consent. In this paper we do three things. First we describe the current confusion and instability in terminology relating to public interest in the context of consent waivers. Second we argue for harmonisation of terminology on the grounds of clarity, transparency and consistency. Third we argue that the term ‘public interest’ best reflects the normative work required to justify consent waivers because it is the broadest of the competing terms. ‘Public interest’ contains within its scope positive and negative implications of a study, as well as welfare, justice and rights considerations. In making this argument, we explain the normative basis for consent waivers, and provide a starting place for further discussion about the precise conditions in which a given study can be said to advance the public interest. Ipsos MORI study found that: … the public would be broadly happy with administrative data linking for research projects provided Those projects have social value, broadly defined. Data are de-identified. Data are kept secure. Businesses are not able to access the data for profit. (shrink)

The existing body of knowledge has witnessed gaps arising from the paucity of research literature on the quality of educational research output in higher education. This study shows how the management of data in higher education affects the quality of academic research conducted by university lecturers in South-South Nigeria. The study adopted a factorial research design. The population of this study includes all the Faculty of Education academic staff in both public and private universities situated in South-South Nigeria. Using (...) results of a survey administered to a sample of 602 accessible lecturers during data collection, one research question and one null hypothesis were resolved in the study. Multiple regression was employed in the analysis of data. Findings revealed amongst others that; data storage, data security, data retrieval, data sharing, and data re-use jointly contributed to the total variance in educational research effectiveness in terms of proper citations by 56.25%, problem-solving by 22.14%, knowledge creation by 34.50%, and generation of testable data by 36.88%; and their composite influence on educational research effectiveness in terms of proper citations, problem- solving, knowledge creation, and generation of testable data, is statistically significant. Based on the findings of this study, it was concluded that data management practices play an important role in higher education that significantly influences the quality of research output in universities in South- South Nigeria. It was recommended among others that, there should be an up-to-date database or data warehouse in Ministries of Education where researchers can easily access data in order to increase the quality of researches by eliminating the problems of data fabrication and falsification. (shrink)

This study assessed two motivational strategies and their respective ties to the service delivery in public universities in Cross River State. In achieving the central and specific targets of this research, four research questions and two null hypotheses were answered and tested in the study. The entire population of 440 security personnel in two public universities was studied, based on the census approach and following the ex-post facto research design. Three sets of expert-validated questionnaires, with Cronbach reliability estimates of (...) 0.80, 0.79 and 0.83 respectively, were used for data collection. Collected data were analysed using descriptive statistics and inferential statistics such as Pearson Product Moment Correlation and the One-way ANOVA. The level of incentives provision and security service delivery in universities was moderate. There were notable disparities between promotion, provision of incentives and security service delivery in universities. Security personnel discharged, on average, services beyond the extent of promotion and incentives provided to them. Provision of incentives did not relate significantly to security service delivery; however, staff promotion significantly influences security service delivery in universities. It was concluded that there are loopholes in the motivational practices of universities for security personnel. The service delivery of security personnel at universities in Cross River State is yet to be at an acceptable point that can promote institutional safety and goal attainment in the short- and long run. It was recommended, among other things, that security personnel who are due for promotion should not be denied such an opportunity, as it represents one of the strongest motivational factors in universities. (shrink)

Health data processing fields face ethical and legal problems regarding fundamental rights. As we know, patients can benefit in the Digital Era from having health or medical information available, and medical decisions can be more effective with a better understanding of clinical histories, medical and health data thanks to the development of Artificial Intelligence, Internet of Things and other Digital technologies. However, at the same time, we need to guarantee fundamental rights, including privacy ones. The complaint about ethical (...) and legal requirements – including constitutional ones – is particularly relevant in the processing of health data. This paper is focused on the problem of the consent required to the processing of health data and the exceptions established in the new European Union General Data Regulation, which cover the processing of this special data -within other aims- for scientific, historical and statistical research as legitimate purposes, which include biomedical research. The conclusion is that these open concepts are problematic both for the protection of privacy rights and for the legal security/certainty of research. On one hand there are several interpretation problems, regarding the processing of health data and the protection of information privacy. On the other hand, professionals must follow the Multilevel legal framework and to guarantee fundamental rights in the processing of health data, so there are also problems of interpretation for researchers. Therefore, we need a clearer legal framework for biomedical research. (shrink)

The article considers the problem of ensuring the economic security of trade enterprises by forming an optimal pricing policy. The methodology of formation the minimum and maximum selling prices of trade enterprise, maintenance of margin of economic security, which is based on research of turnover costs and working capital of trade enterprise is offered. Based on statistical data of trade enterprises, the types of prices by product range are determined, which form a stable economic situation and ensure (...) economic security of trade enterprise. The necessity of forming the optimal price policy and selling prices of trade enterprises with the use of Pareto efficiency tools and construction of single-criteria and multi-criteria tasks, indicators of quality of commodity resources of trade enterprises is substantiated. Using the Excel software processor and the «Regression» function, economic-mathematical models of optimal prices for the product range of trade enterprises are built. The results testified to the effectiveness of the proposed model of the optimal pricing policy of trade enterprises, as the obtained values are within the minimum selling prices, which provides economic security of trade enterprises. The process of forming the pricing policy of trade enterprises to ensure economic security is proposed, which is based on the principles of pricing, appropriate methodological tools and monitoring of market environment factors. Organizational, economic, legal, social and market (marketing) mechanisms for the formation of effective pricing policy of trade enterprises aimed at ensuring economic security are identified. This study is practically interesting for personnel of trade enterprises, regardless of organizationallegal forms of ownership and activities, and theoretically – for researchers dealing with pricing. (shrink)

This special issue of Science and Engineering Ethics is devoted to the ethical, societal and political implications of new and emerging technologies in the area of Human Security. Its aim is to address the wider implications of an altered security landscape. Specifically, and in accordance with SEE’s main area of interest, contributions to this special issue focus on those ethical considerations warranted by scientific and technological advances in the field of human security. This includes, but is not (...) restricted to, issues of privacy and data protection, control, trust, surveillance, authority, and freedom. Papers consider some of the ethical and societal challenges related to new and emerging technologies in the context of the Human Security Doctrine (HSD), as it has been initially formulated by the United Nations Commission on Human Security (UNCHS 2003). The HSD argues for a paradigm shift from understanding security based on tangible assets (e.g., national borders, goo .. (shrink)

The Passenger Name Record (PNR) Directive has introduced a pre-emptive, risk-based approach in the landscape of European databases and information exchange for security purposes. The article contributes to ongoing debates on algorithmic security and data-driven decision-making by fleshing out the specific way in which the EU PNR-based approach to security substantiates core characteristics of algorithmic regulation. The EU PNR framework appropriates data produced in the commercial sector for generating security-related behavioural predictions and does so (...) in a way that gives rise to a paradoxical normativity directly dependent on empirical states. Its ‘securitisation move’ is moreover characterised by an inherent tendence to expand. As a result, the PNR Directive poses challenges for existing check and balance mechanisms and for human autonomy. These challenges could be partially addressed by strengthening ex-post control procedures and independent auditing. Yet in the decision to adopt a risk-based security model, something more fundamental seems to be at stake, namely, the preservation of the idea of human beings as moral agents able to direct and modify their behaviour in accordance with an intelligible, reliable and predictable normative order. (shrink)

Abstract: Wireless security is the avoidance of unlawful access or impairment to computers using wireless networks. Securing wireless network has been a research in the past two decades without coming up with prior solution to which security method should be employed to prevent unlawful access of data. The aim of this study was to review some literatures on wireless security in the areas of attacks, threats, vulnerabilities and some solutions to deal with those problems. It was (...) found that attackers (hackers) have different mechanisms to attack the networks through bypassing the security trap developed by organizations and they may use one weak pint to attack the whole network of an organization. However the author suggested using firewall in each wireless access point as the counter measure to protect data of the whole organization not to be attacked. (shrink)

The oil and gas industry is a complex data-driven industry with compute-intensive, data-intensive and business-intensive features. Cloud computing and big data have a broad application prospect in the oil and gas industry. This research aims to highlight the cloud computing and big data issues and challenges from the informatization in oil and gas industry. In this paper, the distributed cloud storage architecture and its applications for seismic data of oil and gas industry are focused on (...) first. Then,cloud desktop for oil and gas industry applications are also introduced in terms of efficiency, security and usability. Finally, big data architecture and security issues of oil and gas industry are analyzed. Cloud computing and big data architectures have advantages in many aspects, such as system scalability, reliability, and serviceability. This paper also provides a brief description for the future development of Cloud computing and big data in oil and gas industry. Cloud computing and big data can provide convenient information sharing and high quality service for oil and gas industry. (shrink)

The purpose of this paper was to examine institutional variables and the supervision of security in secondary schools in Cross River State. The study specifically sought to determine whether there was a significant influence of school population, school type and school location, on the supervision of security in public secondary schools in Cross River State. Three null hypotheses were formulated accordingly to guide the study. 360 students and 120 teachers resulting in a total of 480 respondents, constituted the (...) sample for the study. The instrument used for data collection was a questionnaire while Independent t-test was used to analyze data and test the hypotheses at .05 level of significance using Microsoft Excel version 2013. The results of the findings revealed that school population, school type and school location, all have an influence in the supervision of security in public secondary schools of Cross River State. It was also revealed that lowly populated, mixed-gender, and urban public secondary schools were more efficient in the supervision of security than their counterparts such as highly populated, single-gender and rural secondary schools. Based on the findings of this study, conclusions were drawn and recommendations were made. (shrink)

This study investigated the perception of how methods of entrepreneurial education predict the level of economic security in the Southern Senatorial District of Cross River State. It was guided by two research questions and two hypotheses, tested at a significant level of 0.05. The research design was a survey. The study had 350 participants. A 40-item "Entrepreneurship Education and Economic Security Questionnaire" (EEESQ) was validated by five experts from the University of Calabar and used in data collection. (...) The EEESQ was pilot tested on 30 respondents who were not a part of the study sample. In order to evaluate the consistency of the EEESQ, Cronbach Alpha statistics were used, and they yielded reliability values of 0.84 and 0.80 for the independent and dependent variables, respectively. Three hundred and fifty respondents responded to the instrument, which was administered. In order to find the answers to the study questions and test the hypotheses, data were analyzed using linear regression analysis at the significant level of .05. The study discovered that experiential and transdisciplinary teaching strongly predict economic security. It was recommended that educators at various stages should introduce students to real-world scenarios when teaching entrepreneurship education. (shrink)

Cloud computing has become an essential component of enterprises and organizations globally in the current era of digital technology. The cloud has a multitude of advantages, including scalability, flexibility, and cost-effectiveness, rendering it an appealing choice for data storage and processing. The increasing storage of sensitive information in cloud environments has raised significant concerns over the security of such systems. The frequency of cyber threats and attacks specifically aimed at cloud infrastructure has been increasing, presenting substantial dangers to (...) the data, reputation, and financial stability of enterprises. Conventional security methods can become inadequate when confronted with ever intricate and dynamic threats. Artificial Intelligence (AI) technologies possess the capacity to significantly transform cloud security through their ability to promptly identify and thwart assaults, adjust to emerging risks, and offer intelligent perspectives for proactive security actions. The objective of this research study is to investigate the utilization of AI technologies in augmenting the security measures within cloud computing systems. This paper aims to offer significant insights and recommendations for businesses seeking to protect their cloud-based assets by analyzing the present state of cloud security, the capabilities of AI, and the possible advantages and obstacles associated with using AI into cloud security policies. (shrink)

The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency (...) and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

This research examines ethical issues in computational linguistics that can be applied to national defense by analyzing philosophical and security language. The increasing use of language contexts, such as intelligence and communication data analysis, raises ethical and philosophical challenges related to privacy, control, and accuracy. This research aims to identify and analyze ethical issues, especially in the use of computational linguistics in defense applications, as well as their implications for the protection of individual rights and privacy. This method (...) involves reviewing ethical technology references and analyzing the philosophical design of language and security. Deontological and utilitarian ethical theories are applied to evaluate the moral impacts and consequences of using the language of technology in a defense context. The results demonstrate that using linguistics in defense can pose privacy risks, misuse of data, and the potential for significant bias in decision-making. This often involves collecting data from multiple sources, creating challenges to ensure data is managed ethically and responsibly. Conclusion: This shows the need for a solid ethical and regulatory framework to ensure technology's fair and responsible use in context defense. Implications of the findings include the need for ongoing dialogue between technology developers, policymakers, and the public to mitigate ethical risks and ensure that application technologies comply with critical and security principles. With a comprehensive and responsible approach, technological linguistics can improve military safety and effectiveness without compromising individual rights and privacy. (shrink)

This study aimed to identify the reality of digital transformation in the Palestinian Ministry of Interior and National Security from the point of view of workers in computer and information technology units. ) employees, and the study tool (the questionnaire) was distributed, and the comprehensive survey method was used, where (61) questionnaires were retrieved with a percentage of (87.1%), and they were unloaded and analyzed using the statistical packages SPSS. The study reached several results, including: The availability of dimensions (...) of digital transformation to a large extent in the Ministry of Interior, Security and National and its amount reached (77.5%). The average overall score for the senior management support dimension scored a large approval degree and a relative weight (78.2%), and The Dimension strategic directions it received a large approval degree and a relative weight (76.3%), The Dimension the technical infrastructure necessary for digital transformation achieved a large approval degree and a relative weight. (73.4%), The Dimension human resources, it scored a large approval degree and a relative weight (72.2%), The Dimension coordination, a large approval degree and a relative weight (82.7%), The Dimension data privacy and security, it scored a large approval degree and a relative weight (80.7%), The Dimension the organizational structure and job description, it received a high degree of approval and a relative weight of (79.3%).The study presented a set of recommendations, the most important of which are: the need for the ministry’s administration to provide a special budget to develop the quality of its electronic services as a lever for digital transformation, the need for the ministry’s administration to spend sufficient amounts on innovation in how to provide its services, for the ministry to have high-speed internet lines and its services available uninterrupted, the need to match the number of Employees in computer and information technology departments and units with the volume and quality of work to bridge the gap between the required performance and actual performance, that the ministry develop a clear methodology for exchanging data and information between the components of the ministry, work on providing information security elements for the uploaded data and files, that the ministry provide a clear and specific job description for the main tasks in Computer and information technology departments/units in the ministry. (shrink)