Unauthorized Data Research Articles

Utilizing AspectJ for Defense Against Evasive Malware Attacks in Android System

Introduction: Mobile devices have become an integral part of our digital lives, facilitating various tasks and storing a treasure trove of sensitive information. However, as more people utilize mobile devices, sophisticated cyber threats are emerging to elude traditional security measures. Method: The use of evasion techniques by malicious actors presents a significant challenge to mobile security, necessitating creative solutions. In this work, we investigate the potential critical role that the aspect-oriented programming paradigm AspectJ can play in strengthening mobile security against evasion attempts. Evasion techniques cover a wide range of tactics, including runtime manipulation, code obfuscation, and unauthorized data access. Results: These tactics usually aim to bypass detection and avoid security measures. In order to address the aforementioned issues, this paper uses AspectJ to give developers a flexible and dynamic way to add aspects to their coding structures so they can monitor, intercept, and respond to evasive actions. It illustrates how AspectJ can improve mobile security and counteract the long-lasting risks that evasion techniques present in a dynamic threat landscape. Conclusion: Consequently, this work proposes a novel defense mechanism incorporating AspectJ into a significant paradigm of security against evasion with 91.33% accuracy and demonstrates the successful detection of evasive attacks.

Heterogeneous effects of information and communication technology development on digital financial consumer protection: Evidence from the global sample

ABSTRACT This study primarily examines the influence of Information and Communication Technology (ICT) on Digital Financial Consumer Protection (DFCP), utilizing a dataset spanning 88 countries globally from 2014 to 2018, within the context of consumer welfare under diverse socioeconomic conditions. The research confirms a positive relationship between information and communication technology (ICT) evolution and digital finance consumer protection (DFCP) in OECD countries with inclusive institutions, competitive markets, and solid educational foundations. Conversely, a negative correlation is observed between ICT growth and DFCP in countries with moderate gender inequality. The study further confirms that ICT development positively influences DFCP due to contextual conditions (i.e. cybersecurity index, political stability, higher educational level, and market quality). Policy implications emphasize the need to address information inequality and protect consumer rights in the digital era. Measures to curb market monopolies of major tech companies and combat unauthorized data usage are recommended. Additionally, enhancing digital literacy is crucial for harnessing the benefits of ICT and promoting financial consumer welfare. Establishing a practical code of conduct aligned with market needs is also essential for ensuring consumer protection.

Enhancing Cloud Computing Security with Blockchain: A Hybrid Approach to Data Privacy and Integrity

This paper investigates the use of blockchain technology to enhance security in cloud computing environments, with a particular focus on data privacy and integrity in multi-tenant systems. The research proposes a hybrid security model that integrates blockchain’s decentralized verification mechanisms with traditional encryption techniques to prevent unauthorized data access and tampering. The system architecture leverages blockchain for immutable transaction logging, while encryption ensures confidentiality. Performance metrics, such as breach detection time, data integrity validation, and computational overhead, are analyzed to assess the model's effectiveness. Experimental results demonstrate that the hybrid model significantly enhances data security in cloud environments by reducing breach detection time and improving data integrity. However, the implementation introduces moderate computational overhead, suggesting the need for further optimization for large-scale applications. Future research should explore scaling solutions such as sidechains or sharding to mitigate these challenges.

Securing Automated Intelligence: Challenges and Solutions in RPA and Generative AI Integration

Integrating Robotic Process Automation (RPA) and Generative AI can revolutionize business processes, enabling greater efficiency, scalability, and intelligent decision-making. However, this powerful combination also introduces various security challenges and impacts to governance that organizations must address to protect sensitive data and maintain trust in automation systems. As RPA bots increasingly interact with AI models, vulnerabilities such as unauthorized data access, malicious model manipulation, and improper handling of sensitive information become more pronounced. These risks can lead to cyberattacks, data breaches, and regulatory compliance violations. This paper examines the security challenges inherent in RPA and Generative AI integration, focusing on three key areas: data privacy, model integrity, and automation governance. We assess how improper configurations and lack of security oversight can expose these systems to exploitation. Furthermore, we explore solutions such as implementing robust encryption protocols, secure data access controls, and continuous monitoring of AI model behavior to detect anomalies. By presenting case studies and evaluating emerging best practices, we offer a framework for safeguarding RPA and AI systems, ensuring that automation remains a trusted and secure tool for organizations. The paper also discusses aligning security strategies with regulatory requirements and industry standards. This approach enables organizations to unlock the full potential of RPA and Generative AI while mitigating risks and protecting against evolving cyber threats.

A robust algorithm for authenticated health data access via blockchain and cloud computing.

In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting.

A Watermark-Based Scheme for Authenticating JPEG 2000 Image Integrity That Complies with JPEG Privacy and Security

Network development has made it easier to access multimedia material and to change it by allowing the collection, modification, and transmission of digital data. Additionally, this has led to a rise in malicious use, including unauthorized data distribution and copying. As the quantity of evil activities increases, security issues such as unauthorized use and image forgery are rising. While security solutions for JPEG-1 images are widely available, there remains a significant gap in protection for JPEG 2000 images. In this paper, we propose a new watermark-based forgery detection method to comply with the JPEG Privacy and Security standards and to authenticate JPEG 2000 image integrity in the discrete wavelet transform (DWT) domain. The method proposed divides JPEG 2000 images into groups of blocks (GOBs) within the DWT domain. The watermark is generated by collaborating with the neighboring GOBs and is embedded in the GOBs. It enables you to respond to the collage attack. Experimental results using various sample images show the superiority of the proposed method, showing negligible visual differences between the original and watermarked JPEG 2000 images.

RETRACTION: An improved hybrid network‐on‐chip with flexible topology and frugal routing

AbstractRETRACTION: Ijaz, Q., Bourennane, E.‐B., “An improved hybrid network‐on‐chip with flexible topology and frugal routing”, The Journal of Engineering. e12395 (2024). https://doi.org/10.1049/tje2.12395The above article, published online on 11th June 2024 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editor‐in‐Chief, James Lam; the Institution of Engineering and Technology; John Wiley & Sons Ltd and the authors of the article.The authors contacted the journal and stated that the following parts of the paper had been inadvertently reproduced from a source [1] without appropriate permission or acknowledgement: FIGURE 3 Architecture of RF interface, in the paper, is taken from, Figure 4.2—Schéma fonctionnel de l'Interface RF of the thesis (page no 63). FIGURE 4 Digital waveguide: An abstraction of five components is taken from Figure 5.1—Modèle de l'interface RF of the thesis (page no 84). Tables 1–3 are tables 6.4–6.6 of the thesis (page no 108–109). Tables 4–6 are tables 6.8–6.10 of the thesis (page no 111–112). The decision for retraction was taken as these unauthorized data are essential to support the conclusions of this manuscript.REFERENCES[1] Brière, A.: Modélisation système d'une architecture d'interconnexion RF reconfigurable pour les many‐cœurs. Architectures Matérielles [cs.AR]. Université Pierre et Marie Curie‐ Paris VI, 2017. Français. NNT: 2017PA066296. tel‐01707801

Concealing And Encrypting: Dual Approaches to Data Security in The Digital Age

Ensuring data security is a paramount concern in the contemporary digital landscape, given the rapid advancements in internet technology leading to a substantial surge in online transfer of both textual and multimedia information. The current modes of communication lack robust security measures, making data vulnerable to unauthorized access during transmission. Recognizing the critical importance of safeguarding electronic data, both public and private sectors employ various procedures and methods. Among the prominent technologies employed for data security are cryptography and steganography. Cryptography involves the art of encrypting information to hide its readable and significant contents, whereas steganography is a technique used to conceal data within a cover media, effectively masking the very existence of the data. It is imperative for organizations to adopt and integrate these security technologies to mitigate the risks associated with unauthorized data access and ensure the integrity of sensitive information.

Blockchain-enabled EHR access auditing: Enhancing healthcare data security

In the realm of modern healthcare, Electronic Health Records EHR serve as invaluable assets, yet they also pose significant security challenges. The absence of EHR access auditing mechanisms, which includes the EHR audit trails, results in accountability gaps and magnifies security vulnerabilities. This situation effectively paves the way for unauthorized data alterations to occur without detection or consequences. Inadequate EHR compliance auditing procedures, particularly in verifying and validating access control policies, expose healthcare organizations to risks such as data breaches, and unauthorized data usage. These vulnerabilities result from unchecked unauthorized access activities. Additionally, the absence of EHR audit logs complicates investigations, weakens proactive security measures, and raises concerns to put healthcare institutions at risk. This study addresses the pressing need for robust EHR auditing systems designed to scrutinize access to EHR data, encompassing who accesses it, when, and for what purpose. Our research delves into the complex field of EHR auditing, which includes establishing an immutable audit trail to enhance data security through blockchain technology. We also integrate Purpose-Based Access Control (PBAC) alongside smart contracts to strengthen compliance auditing by validating access legitimacy and reducing unauthorized entries. Our contributions encompass the creation of audit trail of EHR access, compliance auditing via PBAC policy verification, the generation of audit logs, and the derivation of data-driven insights, fortifying EHR access security.

RiskTree: Decision Trees for Asset and Process Risk Assessment Quantification in Big Data Platforms

The inherent characteristics of big data lies in its voluminous scale, varied data formats, and swift processing velocity. The intrinsic characteristics of big data undermine the efficacy of conventional data security techniques and data management standards, consequently compromising the security of big data. As a consequence, big data possesses susceptibilities to security incidents, including unauthorized data access, data manipulation, and data compromise throughout the transmission, storage, and processing stages. Conventional information system security risk assessment methodologies are constrained by human resources and computational techniques, rendering them unsuitable for direct application to big data platforms. Consequently, there is an urgent necessity to develop a risk assessment framework tailored specifically for big data environments, capable of quantifying potential risks and losses. In response to this need, we have devised an automated risk assessment theory that assimilates the unique characteristics of big data with traditional quantitative methods, introducing a risk metric system suited to the big data context. Utilizing the risk-related data generated during operations on the big data platform, we train a decision tree model to derive the weights for each risk indicator. These weights are then employed to conduct a weighted summation of the operational risk indicators, thereby achieving a quantitative evaluation of the platform's risk profile. To substantiate the proposed framework, experiments were conducted on a simulated big data platform. The experimental outcomes demonstrate that, compared to existing quantitative risk assessment methodologies, our approach enables an automatic, objective, and efficient assessment and quantification of the risks associated with tangible assets and data processing operations within the big data platform.

Exploring Data Privacy and Privacy Paradox Nature of Consumers Using Block Chain Technology: Application of SLR and Bibliometric Analysis Tools

This research presents an in-depth study of blockchain technology’s impact on data privacy and the consumer behaviour phenomenon known as the privacy paradox. Through a systematic literature review (SLR) and bibliometric analysis, the study delves into the effectiveness of blockchain in safeguarding personal data in an increasingly digital world. Key databases such as Scopus and Web of Science provide rich academic contributions, particularly from regions with stringent data privacy laws. The findings highlight blockchain’s effectiveness in limiting unauthorised data access, boosting user control and tackling the privacy paradox, where personal information is shared despite privacy worries. Yet, it points out challenges in aligning blockchain with legal frameworks, such as General Data Protection Regulation, particularly concerning data erasure rights. The study emphasises three themes: blockchain’s progression toward robust data protection, psychological aspects of the privacy paradox in consumer decisions and attitudes toward blockchain solutions. It notes gaps in standardisation, public awareness and blockchain’s practical applications, alongside ethical and policy issues. Conclusively, the research stresses the need for aligning technological advancements with legal and ethical frameworks, highlighting user experience in digital technology adoption. It positions blockchain as a pivotal factor in reshaping digital privacy, advocating for continued innovation and exploration in blockchain applications to ensure a secure, user-empowering future globally.

A lattice-based data sharing functional encryption scheme with HRA security for IoT

In the dynamic and resource-constrained landscape of IoT, securing sensitive data access is paramount. This paper introduces an innovative access control scheme (ACPRE) blending conditional attribute-based proxy re-encryption with inner product encryption, tailored for IoT security needs. ACPRE integrates attribute-based conditions, expands plaintext space, and restricts proxy privileges, bolstering access control. Leveraging inner product encryption, it confines decrypted information to inner product values, ensuring data confidentiality. By limiting proxies’ transformation abilities, ACPRE prevents unauthorized data alterations. It offers a secure, flexible solution for data access management, allowing only entities with specific attributes to utilize inner product values. ACPRE’s lattice-based construction enables resistance against quantum attacks and achieves honestly re-encryption attacks security, fostering robust, privacy-aware IoT ecosystems. Detailed security proofs and experimental validation underscore its efficacy.

Streamlining IoT-driven Data Using Blockchain

The integration of Internet of Things (IoT) technology has revolutionized data collection and analytics, creating vast networks of interconnected devices generating large volumes of data. However, this rapid expansion of IoT ecosystems introduces significant challenges related to data security, integrity, scalability, and privacy. Blockchain technology, with its decentralized and immutable nature, emerges as a compelling solution to address these challenges. This paper explores the synergy between IoT and blockchain, examining how blockchain can streamline IoT-driven data by providing secure data exchanges, enhancing transparency, and facilitating scalability. The study investigates the key mechanisms through which blockchain can mitigate common IoT data issues. It considers blockchain's role in ensuring data integrity by utilizing cryptographic methods, enabling traceability through distributed ledgers, and protecting against unauthorized data manipulation. Furthermore, it explores the potential of blockchain-based smart contracts to automate processes and improve interoperability among IoT devices. The paper delves into specific use cases, such as smart cities, demonstrating how blockchain can effectively streamline IoT data across various industries. The findings highlight that while blockchain offers substantial benefits, it also introduces unique challenges, including high energy consumption, latency concerns, and complex integration processes. This concludes with a discussion of Security concerns, Comparison with existing solutions. By addressing these challenges, blockchain can become a pivotal technology in streamlining IoT-driven data, paving the way for more secure, efficient, and trustworthy IoT ecosystems.

Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology

The paper presents a comprehensive exploration of a novel image encryption and decryption methodology, leveraging finite state machines (FSM) for the secure transformation of visual data. The study meticulously evaluates the effectiveness of the proposed encryption algorithm using a diverse image dataset. The encryption algorithm demonstrates high proficiency in obfuscating the original content of images, producing cipher images that resemble noise, thereby substantiating the encryption’s effectiveness. The robustness of the proposed methodology is further evidenced by its performance in the National Institute of Standards and Technology Statistical Test Suite (NIST STS). Such achievements highlight the algorithm’s capability to maintain the stochastic integrity of encrypted data, a critical aspect of data security and confidentiality. Histogram analysis revealed that the encryption process achieves a uniform distribution of pixel values across the encrypted images, masking any identifiable patterns and enhancing the security level. Correlation analysis corroborated the success of the encryption technique, showing a substantial reduction in the correlation among adjacent pixel values, thereby disrupting spatial relationships essential for deterring unauthorized data analysis. This improvement indicates the algorithm’s efficiency in altering pixel patterns to secure image data. Additionally, a comparative analysis of correlation coefficients using various encryption methods on the Lenna image offered insights into the relative effectiveness of different techniques, emphasizing the importance of method selection based on specific security requirements and data characteristics.

Advancing Privacy and Confidentiality in the Digital Healthcare Era: The Image Encryption Solution

The rapid digitization of health care services has spurred exponential growth in medical image generation and exchange. Safe guarding the confidentiality and integrity of these sensitive E-Health care images is crucial for protecting patient privacy. This project proposes a robust solution within the AWS Cloud, implementing an efficient image encryption model. Advanced cryptographic techniques ensure that only authorized personnel can access and decrypt images, minimizing the risk of unauthorized data breaches. The project aims to advance secure E-Healthcare image sharing, facilitating improved collaboration among providers while prioritizing patient privacy and data integrity.

ADDRESSING PRIVACY AND ETHICAL CONSIDERATIONS IN HEALTH INFORMATION MANAGEMENT SYSTEMS (IMS)

This paper explores the crucial role of privacy and ethics in Health Information Management Systems (HIMS), addressing the inherent challenges and presenting innovative solutions through detailed case studies. We examine the significant privacy concerns, such as data breaches and unauthorized data sharing, alongside ethical issues like informed consent and equitable access to technology. Effective solutions including the Privacy-Preserving Distributed Analytics (PPDA) model and proactive bioethics committees, as demonstrated by institutions like Vanderbilt University Medical Center, illustrate successful strategies for managing these concerns. The paper emphasizes the importance of prioritizing privacy and ethics not merely as compliance requirements but as foundational elements essential to the trustworthiness and effectiveness of HIMS. It advocates for a continuous, proactive approach to address these issues as technology evolves and regulations change. Furthermore, we call for a collaborative effort among policymakers, healthcare providers, technologists, and patients to develop and refine HIMS that uphold the highest standards of privacy, ethics, and accessibility, thus enhancing the quality of care and health outcomes for all stakeholders.

A framework for detection and mitigation of cyber criminal activities using university networks in Kazakhstan

Abstract. The increasing number of information security incidents in higher education underscores the urgent need for robust cybersecurity measures. This paper proposes a comprehensive framework designed to analyze the illegal use of internet resources in university networks in Kazakhstan. The subject of this article is the detection and mitigation of cybercriminal activities using university networks in Kazakhstan. The goal is to develop a comprehensive framework that integrates multiple educational organizations to enhance collaborative security efforts by monitoring network activity and categorizing texts using machine learning techniques. The tasks to be solved are: to formalize the procedure of integrating multiple educational organizations into a collaborative cybersecurity framework; developing a log analysis tool tailored for monitoring network activities within university networks; creating a novel dictionary of extremist terms in the Kazakh language for text categorization; to implement advanced machine learning models for network traffic classification. The methods used are: log analysis tools for real-time monitoring and anomaly detection in network activities, Natural language processing (NLP) techniques to develop a specialized dictionary of extremist terms in Kazakh, Machine learning models to classify network traffic and detect potential cyber threats, and collaborative architecture design to integrate network security efforts across multiple institutions. The following results were obtained: a comprehensive log analysis tool was developed and implemented, providing real-time monitoring of network activities in university networks; a dictionary of extremist terms in Kazakh was created, facilitating the categorization and analysis of texts related to potential security threats; advanced machine learning models were successfully applied to classify network traffic, enhancing the detection and mitigation of cyber threats; and an experimental architecture integrating multiple educational organizations was established, fostering collaborative efforts in cybersecurity. Conclusions. The scientific novelty of the results obtained is as follows: 1) a robust framework for collaborative cybersecurity in educational institutions was developed, leveraging log analysis and machine learning techniques; 2) the creation of a specialized dictionary of extremist terms in Kazakh significantly improved the accuracy of text categorization related to cybersecurity; 3) the application of advanced machine learning models to network traffic classification provided a methodological approach to effectively managing and securing network infrastructure effectively; 4) the experimental architecture demonstrated the potential for enhanced security through collaboration among educational organizations, offering strategic recommendations for improving information security in academic environments. The outcomes of this research contribute to the broader cybersecurity field by providing a structured approach to detecting and mitigating cyber threats in educational contexts. The proposed framework has potential applications extending to global security frameworks, aiming to foster a safer internet usage environment and reduce the risks associated with cyber threats and unauthorized data access.

Comprehensive Swotting on Librem 5

The Librem5 is a privacy-focused smartphone that aims to provide users with a secure and open-source mobile computing experience. Developed by Purism, a company dedicated to creating products that prioritize user privacy and freedom, the Librem5 combines hardware and software features to protect user data and ensure transparency in mobile communications. This article provides an overview of Librem5's key characteristics, highlighting its emphasis on privacy, security, and open-source principles. The smartphone operates on a Linux-based operating system, which offers users complete control over their devices by allowing them to customize, modify, and inspect the underlying software. This approach ensures that users have full visibility into the code running on their devices, mitigating concerns regarding hidden vulnerabilities or unauthorized data collection.

Blockchain-enabled authentication framework for Maritime Transportation System empowered by 6G-IoT

In recent years, Maritime Transportation Systems (MTS) have experienced significant advancements through the adoption of Internet of Things (IoT) technology. The introduction of 6G mobile networks has further expanded possibilities by offering enhanced communication services and additional functionalities such as processing, caching, sensing, and control for a wide array of IoT devices. Despite these technological advancements, the integration of IoT and 6G has introduced new risks and challenges in terms of safety and reliability. The involvement of various maritime stakeholders in scheduling and managing marine transportation exacerbates these challenges. Moreover, the MTS has encountered escalating security and privacy concerns with unauthorized data access and message tampering pose significant vulnerabilities in the 6G-IoT-enabled MTS environment. Therefore, a shared and controlled access mechanism that cannot be manipulated or tampered with by unauthorized parties is also an essential requirement in MTS. To address these issues, this paper presents an authentication framework leveraging blockchain technology, specifically designed for 6G-IoT-enabled MTS. The objective is to handle real-time data using decentralized peer-to-peer cloud servers with minimal latency, all while addressing security and privacy concerns specific to MTS. This integration also serves to mitigate various security threats. The protocol’s security and privacy are verified through rigorous evaluations, including the ROR model, Scyther tool, and informal security analysis. A simulation of the proposed protocol using MIRACL is conducted, offering a comprehensive assessment of its computational costs and security features when compared to existing protocols, demonstrating its superior security and efficiency.

Protection of Citizens' Personal Data as Voters in the Organization of Simultaneous General Elections in 2024 (A Study in the East Lombok Election Commission)

This research aims to examine the Legal Protection of Voters' Personal Data in the Implementation of the Simultaneous General Elections in 2024. There are several problem formulations in this research, including the following: What are the forms of Legal Protection of Voters' Personal Data in the Implementation of the Simultaneous General Elections in 2024? How is the implementation of the Protection of Citizens' Personal Data as Voters in the Implementation of the Simultaneous General Elections in 2024 in the East Lombok Election Commission (KPU Lombok Timur)? The Legal Protection of Voters' Personal Data in the Implementation of the Simultaneous General Elections in 2024 takes the form of legal protection through regulations such as Law No. 7 of 2017 regarding general elections and the Personal Data Protection Act No. 27 of 2022. This can be considered as a form of protection for electoral participants' data from regulations governing unauthorized data usage, misuse of data for specific interests, and data tampering. The Implementation of the Protection of Citizens' Personal Data as Voters in the Implementation of the Simultaneous General Elections in 2024 in the East Lombok Election Commission involves two steps: preventive and repressive measures. The preventive steps taken by the East Lombok Election Commission include data protection, starred NIK markers, information transparency, data accuracy and updating, and cooperation with relevant institutions. The repressive steps include enforcement against personal data theft, enhancement of cybersecurity, and Governance of Personal Data Protection Policy.