Protection Of Sensitive Information Research Articles

Smart Defense: How Self-Learning AI Can Shield Bangladeshi Medical Records

The digitalization of healthcare records in Bangladesh presents both opportunities and challenges, particularly concerning the security and protection of sensitive patient information. As electronic health records (EHRs) become increasingly prevalent, the threat of cyberattacks targeting medical data escalates, necessitating innovative solutions to fortify the country's healthcare cybersecurity infrastructure. This paper investigates the efficacy of self-learning artificial intelligence (AI) systems in safeguarding Bangladeshi medical records against cyber threats. The traditional methods of securing medical records, such as firewalls and antivirus software, are proving inadequate against the evolving tactics of cybercriminals. Bangladesh faces unique challenges in this regard, including limited resources, lack of cybersecurity awareness among healthcare professionals, technological fragmentation, and an increasingly sophisticated threat landscape. To address these challenges, there is a growing imperative to explore novel approaches that can adapt and evolve in real-time to counter emerging cyber threats. Self-learning AI systems represent a promising frontier in healthcare cybersecurity. By leveraging advanced machine learning algorithms, these systems can analyze vast amounts of data to detect patterns indicative of cyber threats. Unlike static security measures, self-learning AI continuously learns from new information and adjust their defense strategies, accordingly, enabling them to stay ahead of evolving threats. Key functionalities of self-learning AI include anomaly detection, threat prediction, and adaptive defense mechanisms, all of which are essential for safeguarding medical records in Bangladesh's healthcare landscape. The implications of integrating self-learning AI into Bangladesh's healthcare cybersecurity framework are significant. Not only can these technologies enhance the detection and prevention of cyber threats, but they can also alleviate resource constraints and technical challenges faced by healthcare organizations. However, successful implementation requires comprehensive training, adherence to data privacy regulations, and ongoing monitoring to ensure the effectiveness and reliability of AI-driven security measures. The protection of medical records is paramount as Bangladesh continues its digital transformation in healthcare. Self-learning AI offers a dynamic and proactive approach to cybersecurity, empowering healthcare organizations to mitigate risks and preserve patient privacy in an increasingly digitized landscape. Embracing these innovative technologies is crucial for building a resilient healthcare ecosystem that prioritizes data security and patient trust.

A Two-Level Security Layer for Medical Data

Implementing a two-level security approach comprising cryptography and steganography in Electronic Health Record (EHR) systems ensures comprehensive protection of sensitive patient information. Cryptography encryption methods alone may not provide adequate protection, as they can be susceptible to attacks and also, steganography, has the visual quality issues after embedding. The combination of RSA cryptography and DWT steganography will offers a comprehensive solution for securing medical data, ensuring confidentiality, integrity, and compliance with privacy regulations, while DWT steganography provides covert embedding within medical images, preserving diagnostic integrity and enhancing data protection. In this paper, a secure two-level layer of medical data security is proposed to encrypt secret information using Rivest-Shamir-Adleman (RSA) while the encrypted secret information is embedded using Discrete Wavelet Transform (DWT). The performance of the proposed method was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NCC) metrics. The results obtained show that our method performs with a value of 0.105 for MSE, 49.713 for PSNR and 0.912 for NCC showing high visual quality and low imperceptibility.

An Analytical Predictive Model and Secure Wed Based Personalized Diabetes Monitoring System using Stacking Ensemble Classification

As the number of people diagnosed with diabetes continues to rise, this study takes a groundbreaking approach by developing a secure web-based personalized diabetes monitoring system that incorporates analytical prediction models. This groundbreaking technology was developed in response to the critical need for sophisticated monitoring solutions that address the unique demands of each patient. The suggested method aims to transform diabetes treatment by using predictive modeling to anticipate diabetic trends and possible consequences. The study demonstrates a strong commitment to security by creating a web-based platform that handles patient data with the highest level of care. By resolving important privacy problems and creating a trustworthy environment for users, this secure framework guarantees the protection of sensitive health information. This approach takes use of several models' characteristics to make diabetes trend estimates more accurate and reliable. In addition to improving the system's prediction skills, stacking ensemble classification helps it adapt to different patient profiles. Because of the importance of accessibility and usability in encouraging patient participation, the suggested solution revolves around the creation of a user-friendly online interface. The interface is a living, breathing platform that allows for frictionless communication between healthcare practitioners and their patients. With the help of tailored insights and trend predictions, patients are better able to manage their diabetes. At the same time, doctors and hospitals have access to all the patient information they need, which allows them to take better, more preventative measures.

Ethical and legal aspects of cybersecurity in health

Background: With the emergence of eHealth and mHealth, the use of mental health apps has increased significantly as an accessible and convenient approach as an adjunct to promoting well-being and mental health. There are several apps available that can assist with mental health monitoring and management, each with specific features to meet different needs. The intersection of mental health and cyber technology presents a number of critical legal and ethical issues. As mental health monitoring apps and devices become more integrated into clinical practice, cybersecurity takes on paramount importance. Objective: To address the ethical and legal aspects of health cybersecurity related to applications in mental health monitoring and management. Methods: We carried out a thematic synthesis of the best scientific evidence. Results: These tools have the potential to significantly improve access to and quality of care for users with mental health conditions, but they also raise substantial concerns about privacy and informed consent.  Cybersecurity in mental health is not only a matter of technology, but also of human rights. The protection of sensitive mental health information is critical, and legal and ethical measures to safeguard this information must be implemented in a robust and transparent manner. Conclusion: the use of information technologies and mobile devices is now part of the clinical reality and its future perspectives. It is important to mention that while these apps can be helpful for self-care and mental well-being management, they are not a substitute for the advice and support of a qualified mental health professional (psychologist or psychiatrist). As we move into the digital age, it is imperative that mental health monitoring and management apps are developed and used responsibly, ensuring the safety, dignity, and well-being of users.

JAVA Based Quiz Application

The Java-based Quiz Application is a versatile, standalone program designed to operate independently of an internet connection, making it ideal for offline use in various settings such as training sessions, schools, and areas with limited internet access. It boasts a comprehensive set of features for quiz creation, administration, and participation. Key among its features is the offline user authentication system, enabling users to securely create accounts and log in without internet connectivity. This ensures accessibility and autonomy for users regardless of their location or internet availability. The application offers a user-friendly interface for adding various question types, including multiple choice, to offline quizzes, allowing users to customize quizzes to suit their needs and cover diverse topics. Furthermore, users can manage quizzes offline, facilitating real-time adjustments by educators, trainers, or organizers without the need for an internet connection. The application provides players with a seamless offline quiz experience, offering swift performance feedback and navigation to enhance engagement. Users can select quizzes from offline categories, attempt them offline, and receive immediate scoring and feedback. Emphasizing security, the Java Quiz Application implements robust encryption techniques to safeguard user data and ensure confidentiality offline. This commitment to security ensures the protection of sensitive information even in offline environments, maintaining user trust and confidence in the application's integrity. Key Words: Java, No Internet Connection, Offline Quiz, user Friendly

Automatic Translation of English Terms for Computer Network Security Based on Deep Learning

Translation computer network security refers to the protection of sensitive information during the transmission of data across networks in different languages. With the increasing globalization of businesses and communications, the need for secure translation services has become paramount. Security measures such as encryption, authentication, and data integrity verification play a crucial role in safeguarding translations from unauthorized access, interception, or tampering. Additionally, the use of secure communication protocols and encryption algorithms ensures that data remains confidential and protected from cyber threats. This paper presents an automatic translation approach for English terms related to computer network security, leveraging deep learning techniques with Cryptographic Hashing Authentication Classification (CHAC). The proposed framework aims to enhance the accuracy and efficiency of translating security terms across different languages, facilitating effective communication and collaboration in cybersecurity contexts. Through simulated experiments and empirical validations, the effectiveness of the CHAC-enhanced deep learning model is evaluated, demonstrating significant improvements in translation accuracy and performance. For instance, the CHAC model achieved an average accuracy rate of 90% in translating security terms, outperforming traditional translation methods by 20%. Additionally, the framework reduced translation time by 30%, streamlining the process of generating multilingual security documentation and communications. These results underscore the potential of deep learning with CHAC in automating the translation of English terms for computer network security, enhancing global cybersecurity efforts and facilitating cross-cultural collaboration.

A Study on Automated Hospital Management System in Nigeria

This thesis underscores the critical importance of effective data management in the operational and survival aspects of contemporary hospitals. Acknowledging the imperative for continuous enhancements in this domain, the study recommends a proactive approach to explore and advance the existing hospital data management project by transitioning it to an online platform. This strategic move includes the implementation of robust security measures, particularly a secure access code system, to fortify the protection of sensitive information. The proposed transition aims not only to modernize the hospital's data management capabilities but also to establish a secure online infrastructure, guarding against unauthorized access and ensuring the integrity and confidentiality of critical healthcare data. The thesis concludes with a comprehensive overview of the development process for the computerized system. A detailed survey of the existing system laid the groundwork for the meticulous design of a database application focused on facilitating the search and retrieval of patient folders. The anticipated automated hospital management system is poised to significantly enhance the overall efficiency of healthcare services for both nurses and doctors, introducing notable improvements such as reduced time spent on locating patient folders, increased accuracy, and timely record preparation. Throughout the implementation phase, substantial efforts were dedicated to eliminating data redundancy and inconsistency, with a specific emphasis on enhancing the integrity of the stored data. The chosen software model, the waterfall model, provided a systematic and sequential approach to software development. Additionally, a structured design methodology was employed during the design stage to ensure clarity and efficiency. Recognizing the sensitivity of healthcare data, the thesis rigorously addresses security concerns by incorporating robust measures into the computerized system. These measures are designed to prevent unauthorized access, safeguarding the confidentiality and privacy of stored data. This multi-faceted approach to system development underscores the commitment to delivering a robust, efficient, and secure automated hospital management system aligned with the evolving needs of healthcare professionals, guaranteeing top-tier levels of data integrity and security.

MedConnect PLUS: Health Management System

Abstract: The utilization of mobile applications in healthcare represents a frontier of innovation with the potential, to significantly enhance patients' daily healthcare management. These applications have the capacity to improve the efficiency of healthcare delivery, ultimately leading to cost reduction, and play a pivotal role in disease prevention by influencing behavior change. The paper introduces a model for an interactive healthcare application designed to meet the collaborative needs of both doctors and patients. The envisioned mobile application seeks to optimize communication among various stakeholders in the healthcare sector, including doctors, pharmacists and patients, thereby enhancing overall healthcare service and delivery. Object Oriented Analysis is employed in the software development process. This proposed mobile application is geared towards facilitating effective communication by incorporating advanced technologies to streamline processes such as appointment scheduling, health record management, and patient-doctor interactions. Emphasis is placed on the application's commitment to data security and compliance with healthcare regulations, ensuring the protection of sensitive patient information within a secure environment. The user-friendly interfaces tailored for both patients and doctors highlight the application's intention to cultivate a more interconnected and accessible healthcare ecosystem.

A comprehensive examination of security and privacy in precision agriculture technologies

Precision agriculture has revolutionized modern farming practices by integrating cutting-edge technologies such as sensors, drones, and data analytics to optimize crop management. While these advancements offer unprecedented benefits in terms of yield optimization and resource efficiency, they also raise significant concerns regarding the security and privacy of sensitive agricultural data. This research paper delves into the intricate landscape of precision agriculture security and privacy, aiming to identify potential threats, vulnerabilities, and the corresponding measures necessary for safeguarding farmers' data and ensuring the sustainability of precision farming practices. The study employs a multidisciplinary approach, combining insights from computer science, agriculture, and privacy studies to provide a comprehensive overview. Through an analysis of existing security frameworks and privacy regulations, this paper proposes recommendations for the development and implementation of robust security protocols and privacy-enhancing technologies tailored to the unique challenges of precision agriculture. The findings of this research contribute to the ongoing dialogue on responsible technology adoption in agriculture, emphasizing the importance of striking a balance between innovation and the protection of farmers' sensitive information in the era of precision farming.

JPEG-compatible Joint Image Compression and Encryption Algorithm with File Size Preservation

Joint image compression and encryption algorithms are intensively investigated due to their powerful capability of simultaneous image data compression and sensitive information protection. Unfortunately, most of the existing algorithms suffered from either poor compression efficiency or weak encryption strength, making them vulnerable to cryptanalysis. To address these limitations, we propose a chaos-based JPEG-compatible joint image compression and encryption algorithm. We separate the luminance and chrominance coefficients to preserve file size and encrypt the discrete cosine transform (DCT) coefficients in parallel. The proposed inter-block DC encryption strategy achieves high encryption intensity based on the permutation-substitution structure. In addition, we apply both inter- and intra-block permutations to AC coefficients and strengthen the encryption using an inter-block substitution for non-zero AC coefficients. The results of security and performance analyses demonstrate that the proposed algorithm offers robust encryption of image data while maintaining compression efficiency for real-time transmission.

Implementation Of The Arnold Catmap On A Combination Of Symmetric And Asymmetric Cryptography

The escalating need for robust data security has propelled cyber security practitioners into a perpetual quest for innovative solutions. This endeavor involves the strategic amalgamation of cryptographic algorithms with meticulously customized alterations to specific algorithmic processes. In this pursuit of heightened data protection, the Arnold Cat Map emerges as a pivotal tool, a mathematical transformation that gracefully elucidates the intricate movement of points within a two-dimensional plane. This movement occurs in a systematic and repetitive manner, rendering it an indispensable asset in the domains of cryptography and image scrambling. The Arnold Cat Map operates by meticulously relocating each point within the two-dimensional plane to a fresh coordinate, all the while adhering to an intricately structured pattern. The result is a formidable "mixing" effect that enhances data security. When applied theoretically to widely employed encryption methods like Advanced Encryption Standard (AES) for symmetric encryption and the Rivest-Shamir-Adleman (RSA) algorithm for asymmetric encryption, the Arnold Cat Map exhibits the potential to significantly augment the randomness of the encrypted output. This augmentation of randomness, in turn, fortifies the security of digital assets and communications, making them more resilient against adversarial attacks. By introducing this innovative concept into the realm of cryptography, cyber security practitioners endeavor to fortify data security, offering a higher degree of confidence in the protection of sensitive information and digital assets against a backdrop of ever-evolving cyber threats.

DETECTION, ANALYSIS AND PROTECTION OF CONFIDENTIAL DATA USING AMAZON MACIE MACHINE LEARNING TECHNOLOGY

Over the past decades, the field of data storage and processing has undergone significant changes and expansion, especially with the advent of cloud technologies and computing. Cloud services enable organizations to store and access large amounts of data through distributed systems. However, along with these new opportunities come new challenges, particularly in the area of protecting confidential data. Protecting sensitive data is an extremely important task for today's organizations, especially in the face of a growing number of digital threats and security breaches. In order to ensure reliable protection of valuable and sensitive information, developers and researchers are actively working on the development of new technologies and tools. One of the powerful tools used to identify, analyze and protect confidential data is the machine learning technology of the Amazon Macie service. Amazon Macie is an AWS cloud computing service that uses artificial intelligence and machine learning algorithms to automate data analysis and identify potential data security threats. The main purpose of this work is the detection, analysis and protection of confidential data using Amazon Macie machine learning technology. Amazon Macie is an innovative service developed by Amazon Web Services (AWS) that uses advanced machine learning algorithms for automated discovery and analysis of sensitive data. As part of the work, an analysis of the main machine learning algorithms, principles of data storage systems and methods of protecting confidential information was carried out. The working principles and capabilities of Amazon Macie, which uses advanced machine learning algorithms for automated data analysis and detection of potential threats to data security, were investigated.

A Multi-Faceted Encryption Strategy for Securing Patient Information in Medical Imaging

In modern healthcare, transmitting digital medical images through open-source networks for diagnosis in remote centers poses a significant security challenge due to the sensitive patient information involved. This paper presents an algorithm designed to encrypt these crucial medical images comprehensively. The algorithm generates hash code to validate image integrity, followed by feature-based watermarking, transformation into the frequency domain via Discrete Cosine Transform (DCT), and encryption using Advanced Encryption Standard (AES) and RSA encryption techniques. Each step plays a pivotal role in fortifying the images against unauthorized access, tampering, or interception during transmission, Guaranteeing the secrecy, unaltered state, and legitimacy of the included medical information. Implementing this encryption algorithm mandates strict adherence to cryptographic best practices, robust execution of encryption algorithms, secure key management, and compliance with industry standards. These meticulous measures bolster the confidentiality and integrity of medical images, which are crucial in protecting patient privacy and maintaining data integrity within healthcare systems. This comprehensive encryption strategy addresses the need for the purpose of ensuring safe and protected delivery and protection of sensitive medical information across networks.

Research of Cryptographic Information Protection Technology in Health and Medical Care Field of China

Cryptography technology based on domestic algorithms for the use of informatization in the health and medical care field of China is classified and researched in the paper. In the trend of medical services gradually transitioning from "closed" to "open", the medical information security environment is not optimistic. It is emphasized that with the correct settings and selection of the necessary cryptographic stability of the encryption algorithm, the technology is safe and independent, ensures the safety of medical data and facilitates the circulation of medical services.  Cryptography technology service realizes the identity authenticity of medical data access subjects, the confidentiality and integrity protection of medical health information in the process of sharing and storage, the protection of sensitive information in the process of access, query and use, and the secure data cochain in the whole process, and ensures that the cryptographic application is secure, independent and controllable, truly achieves the security of medical health data and promotes the open sharing of health services

A Multi-Agent Deep-Reinforcement-Learning-Based Strategy for Safe Distributed Energy Resource Scheduling in Energy Hubs

An energy hub (EH) provides an effective solution to the management of local integrated energy systems (IES), supporting the optimal dispatch and mutual conversion of distributed energy resources (DER) in multi-energy forms. However, the intrinsic stochasticity of renewable generation intensifies fluctuations in the system’s energy production when integrated into large-scale grids and increases peak-to-valley differences in large-scale grid integration, leading to a significant reduction in the stability of the power grid. A distributed privacy-preserving energy scheduling method based on multi-agent deep reinforcement learning is presented for the EH cluster with renewable energy generation. Firstly, each EH is treated as an agent, transforming the energy scheduling problem into a Markov decision process. Secondly, the objective function is defined as minimizing the total economic cost while considering carbon trading costs, guiding the agents to make low-carbon decisions. Lastly, differential privacy protection is applied to sensitive data within the EH, where noise is introduced using energy storage systems to maintain the same gas and electricity purchases while blurring the original data. The experimental simulation results demonstrate that the agents are able to train and learn from environmental information, generating real-time optimized strategies to effectively handle the uncertainty of renewable energy. Furthermore, after the noise injection, the validity of the original data is compromised while ensuring the protection of sensitive information.

Examining Sensitive Personal Information Protection in China: Framework, Obstacles, and Solutions

Examining Sensitive Personal Information Protection in China: Framework, Obstacles, and Solutions

Providing Authenticity and Integrity for Disseminated Data Items in Wireless Sensor Networks

A Wireless sensor Network (WSN) may also be a wireless network comprising a number of sensor nodes and is used to monitor both the environmental and physical status of a given area. WSN constitute one of the most important technologies in each of the most important applications. Unit of measurement for WSN that are susceptible to security risks. Multiple Protocols are being developed to make them secure. The protection of sensitive information is the primary focus of many of the protocols that make up the major. The term given to these protocols is the knowledge discovery and dissemination protocols. Distributing management instructions and changing configuration settings on sensor nodes is accomplished with the help of the data detection and distribution protocol for WSN. Each and every one of the currently used protocols for the discovery and distribution of information has two major shortcomings. To a large extent, they encourage a centralized method (only extremely inexpensive stations are able to disseminate data item), and this is the case. This strategy is not suitable for situations in which there are numerous owners and various users. Second, security was not a consideration throughout the development of the protocols. This article proposes the first distributed information detection and distribution protocol, known as DiDrip, that is more secure than existing ones. The protocol allows numerous homeowners to authorize several network users with entirely different priorities simultaneously and immediately flow into information items to sensor nodes. This may be done by multiple homeowners simultaneously using the same protocol.

Mining multi-center heterogeneous medical data with distributed synthetic learning

Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the protection of sensitive personal information. DSL enables the building of a homogeneous dataset with entirely synthetic medical images via a form of GAN-based synthetic learning. The proposed DSL architecture has the following key functionalities: multi-modality learning, missing modality completion learning, and continual learning. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider by the use of an ideal synthetic quality metric called Dist-FID. We show that DSL can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55% and the temporal datasets segmentation model by 8%.

A biometric‐based implicit authentication protocol with privacy protection for ubiquitous communication environments

SummaryDue to the proliferation of the wireless communication technologies, we are now in the era of ubiquitous computing. People can enjoy services almost in any time and any location based on the mobile intelligent devices. Meanwhile, more and more personal information are transmitted through the network. To protect these sensitive information, authentication and privacy protection are primary concerns in ubiquitous communication environments. However, traditional physical‐based biometric authentication scheme usually requires explicit cooperation of the user, which is inconvenient to the users. In order to solve this problem, a behavior biometric authentication protocol with privacy protection is designed using matrix multiplication and homomorphic encryption for ubiquitous communication environments. The correctness, security, and privacy protection properties are proved with security analysis. Performance evaluation shows that our protocol achieves stronger security and higher communication efficiency compared with other related protocols, which makes it very suitable for ubiquitous communication environments because communication consumes much more energy than computation does in mobile communication.

The Foreign Office ‘Thought Police’: Foreign Office Security, the Security Department and the ‘Missing Diplomats’, 1940 – 1952

ABSTRACT The protection of diplomats, embassies and sensitive information has always been an important aspect of diplomacy. Today, security is an accepted norm of day-to-day diplomatic work, yet the importance of security in the UK Foreign Office was not always appreciated, with the department witnessing embarrassing security lapses and scandals during the first half of the Twentieth Century. This article highlights the importance of security to diplomacy, offering the first significant study of the origins and early development of the Foreign Office’s Security Department, established in 1946. It also explores the tensions between security officials and the wider Foreign Office, which indicate the extent to which organisational and internal cultural issues stymied good diplomatic security, issues that were laid bare in the aftermath of the defection of Foreign Office officials Guy Burgess and Donald Maclean in 1951.