-The integration of Artificial Intelligence (AI) in mobile health (mHealth) devices offers significant advancements in patient care but also raises complex security and privacy concerns for sensitive health data. This paper explores the evolving landscape of Health Insurance Portability and Accountability Act (HIP AA) compliance within the context of AI-based mHealth devices, focusing on anticipated challenges in 2024. It examines the current state of HIP AA compliance in AI-driven mHealth, identifying potential vulnerabilities and gaps among mHealth devices in considering privacy and security. The study outlines key security and privacy considerations specific to AI-powered mHealth technologies, emphasizing risks such as data breaches, ransomware attack and unauthorized access. The paper concludes by highlighting the importance of continual adaptation to the dynamic nature of AI technologies and offers insights for stakeholders to navigate the regulatory landscape and ensure the responsible deployment of AI in healthcare.

Falls among the elderly are a major health concern, frequently resulting in serious injuries and a reduced quality of life. In this paper, we propose "BlockTheFall," a wearable device-based fall detection framework which detects falls in real time by using sensor data from wearable devices. To accurately identify patterns and detect falls, the collected sensor data is analyzed using machine learning algorithms. To ensure data integrity and security, the framework stores and verifies fall event data using blockchain technology. The proposed framework aims to provide an efficient and dependable solution for fall detection with improved emergency response, and elderly individuals’ overall well-being. Further experiments and evaluations are being carried out to validate the effectiveness and feasibility of the proposed framework, which has shown promising results in distinguishing genuine falls from simulated falls. By providing timely and accurate fall detection and response, this framework has the potential to substantially boost the quality of elderly care.

Mobile wearable health devices have expanded prevalent usage and become very popular because of the valuable health monitor system. These devices provide general health tips and monitoring human health parameters as well as generally assisting the user to take better health of themselves. However, these devices are associated with security and privacy risk among the consumers because these devices deal with sensitive data information such as users sleeping arrangements, dieting formula such as eating constraint, pulse rate and so on. In this paper, we analyze the significant security and privacy features of three very popular health tracker devices: Fitbit, Jawbone and Google Glass. We very carefully analyze the devices' strength and how the devices communicate and its Bluetooth pairing process with mobile devices. We explore the possible malicious attack through Bluetooth networking by hacker. The outcomes of this analysis show how these devices allow third parties to gain sensitive information from the device exact location that causes the potential privacy breach for users. We analyze the reasons of user data security and privacy are gained by unauthorized people on wearable devices and the possible challenge to secure user data as well as the comparison of three wearable devices (Fitbit, Jawbone and Google Glass) security vulnerability and attack type.

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

Outlier reduction is crucial in computer science for improving data quality, analysis accuracy, and modeling robustness. Selection and modification of DBSCAN parameters are essential for optimal clustering accuracy and outlier detection. We developed an adaptive technique to minimize outliers in the DBSCAN algorithm using a linear congruential method (LCM) to determine values of Epsilon (Eps) and Min-Points (MinPts), known as modified DBSCAN (M-DBSCAN). To enhance the DBSCAN method, we create integer random numbers for MinPts (1--100) and floating numbers for Eps (0.1--1.5) using LCM. We adjusted parameter lists to reduce outliers based on MinPts and Eps values. We choose parameters based on dataset features and requirements, balancing clustering sensitivity and noise treatment. For experiment result analysis we use the Silhouette Score (SS) method. M-DBSCAN improved all cases and it has 50% poorer outlier accuracy than DBSCAN.

The advent of mobile health applications (mHealth apps) has significantly altered the landscape of personal health management. Particularly, mHealth apps developed with the Laravel framework have gained popularity due to their robustness and scalability. However, this convenience comes with the increased responsibility of safeguarding Protected Health Identifier (PHI). The Health Insurance Portability and Accountability Act (HIPAA) sets forth standards for entities handling PHI to ensure privacy and security. Despite these critical regulations, many Laravel developers lack in-depth knowledge of HIPAA compliance, leading to potential vulnerabilities. This work introduces a comprehensive evaluation framework for assessing Laravel-based mHealth apps’ HIPAA Technical compliance. Our framework provides developers with analytical tools to review their source code for compliance issues and guide them in implementing robust security features. Through static code analysis, the framework examines access control, audit controls, integrity, authentication, encryption, transmission security, user inactivity monitoring, and data lifecycle management. To validate our framework’s effectiveness, we developed a specialized web-based tool and conducted an empirical analysis of 200 Medical and Health Fitness category Laravel applications from Github. The analysis revealed significant compliance gaps, particularly in user authorization, data protection, audit controls, and transmission security. In response, we offer detailed recommendations for developers to address common pitfalls and adhere to HIPAA standards. Additionally, the framework has been used to develop a secured web-based portal to assist consumers in evaluating the privacy and security of mHealth apps.