Security and Privacy Analysis of Wearable Health Device

Wearable technology allows for consumers to record their healthcare data for either personal or clinical use via portable devices. As advancements in this technology continue to rise, the use of these devices has become more widespread. In this paper, we examine the significant security and privacy features of three health tracker devices: Fitbit, Jawbone and Google Glass. We also analyze the devices\u27 strength and how the devices communicate via its Bluetooth pairing process with mobile devices. We explore possible malicious attacks through Bluetooth networking. The outcomes of this analysis illustrate how these devices allow third parties to access sensitive information, such as the device exact location, which causes the potential privacy breach for users. We analyze and compare how unauthorized parties may access the user data and the challenges to secure user data on three wearable devices (Fitbit, Jawbone, and Google Glass) security vulnerability and attack type

BlockTheFall: Wearable Device-based Fall Detection Framework Powered by Machine Learning and Blockchain for Elderly Care

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.Comment: Accepted to publish in The 1st IEEE International Workshop on Digital and Public Healt

Secure Mobile Application Development with Data Leak Analysis Plugin

As the mobile devices and applications are being widely adopted and used, Mobile security, or more specifically mobile device security, has become increasingly important. Attacks through mobile apps have caused major data leak due to application vulnerabilities continue to occur. This kind of data leaks can be addressed and fixed in the application development process. Many developers may not be well aware of the vulnerabilities when developing mobile applications and may lack the technical support for detecting data leak analysis within the development environment. In this report, we discuss the need and development of a plugin tool for Android Studio for preventing data leak. We developed a hands-on labware where the plugin can be applied for detection of data leak through SQL injection. We also shared our ongoing experience of the labware integrated in a Summer 2020 course. The preliminary student feedback is collected and reported in this document

Secure Mobile Application Development with Data Leak Analysis Plugin

As the mobile devices and applications are being widely adopted and used, Mobile security, or more specifically mobile device security, has become increasingly important. Attacks through mobile apps have caused major data leak due to application vulnerabilities continue to occur. This kind of data leaks can be addressed and fixed in the application development process. Many developers may not be well aware of the vulnerabilities when developing mobile applications and may lack the technical support for detecting data leak analysis within the development environment. In this report, we discuss the need and development of a plugin tool for Android Studio for preventing data leak. We developed a hands-on labware where the plugin can be applied for detection of data leak through SQL injection. We also shared our ongoing experience of the labware integrated in a Summer 2020 course. The preliminary student feedback is collected and reported in this document