Privacy Leakage of Physical Activity Levels in Wireless Embedded Wearable Systems

International audienceWith the ubiquity of sensing technologies in our personal spaces, the protection of our privacy and the confidentiality of sensitive data becomes a major concern. In this paper, we focus on wearable embedded systems that communicate data periodically over the wireless medium. In this context, we demonstrate that private information about the physical activity levels of the wearer can leak to an eavesdropper through the physical layer. Indeed, we show that the physical activity levels strongly correlate with changes in the wireless channel that can be captured by measuring the signal strength of the eavesdropped frames. We practically validate this correlation in several scenarios in a real residential environment, using data collected by our prototype wearable accelerometer-based sensor. Lastly, we propose a privacy enhancement algorithm that mitigates the leakage of this private information

Side channel attacks on smart home systems: A short overview

This paper provides an overview on side-channel attacks with emphasis on vulnerabilities in the smart home. Smart homes are enabled by the latest developments in sensors, communication technologies, internet protocols, and cloud services. The goal of a smart home is to have smart household devices collaborate without involvement of residents to deliver the variety of services needed for a higher quality of life. However, security and privacy challenges of smart homes have to be overcome in order to fully realize the smart home. Side channel attacks assume data is always leaking, and leakage of data from a smart home reveals sensitive information. This paper starts by reviewing side-channel attack categories, then it gives an overview on recent attack studies on different layers of a smart home and their malicious goals

xLED: Covert Data Exfiltration from Air-Gapped Networks via Router LEDs

In this paper we show how attackers can covertly leak data (e.g., encryption keys, passwords and files) from highly secure or air-gapped networks via the row of status LEDs that exists in networking equipment such as LAN switches and routers. Although it is known that some network equipment emanates optical signals correlated with the information being processed by the device ('side-channel'), intentionally controlling the status LEDs to carry any type of data ('covert-channel') has never studied before. A malicious code is executed on the LAN switch or router, allowing full control of the status LEDs. Sensitive data can be encoded and modulated over the blinking of the LEDs. The generated signals can then be recorded by various types of remote cameras and optical sensors. We provide the technical background on the internal architecture of switches and routers (at both the hardware and software level) which enables this type of attack. We also present amplitude and frequency based modulation and encoding schemas, along with a simple transmission protocol. We implement a prototype of an exfiltration malware and discuss its design and implementation. We evaluate this method with a few routers and different types of LEDs. In addition, we tested various receivers including remote cameras, security cameras, smartphone cameras, and optical sensors, and also discuss different detection and prevention countermeasures. Our experiment shows that sensitive data can be covertly leaked via the status LEDs of switches and routers at a bit rates of 10 bit/sec to more than 1Kbit/sec per LED

Discrete Chaotic Fuzzy Neural Network (DC-FNN) Based Smart Watch Embedded Devices for Sports and Health Monitoring

Improved athletic performance is expected to result from the convergence of semiconductor technology from the wearable device equipped with physiology and its clinical and translation tools. The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services.This research optimises the usage of smart watch integrated devices through wireless connection, which sheds light on wearable sensors used in sports medicine. The major objective of this article is to provide a recommended method of using wearable technology for evaluating the efficacy of health and sports monitoring. Any sport at any level may stand to profit from this embedded technology, as might academic research labs, sports medicine practises, and professional sports teams all working toward the same goal of improving player and team performance. As the primary data generated by wearable devices include the heartbeat rate, step count, and energy consumed, researchers have concentrated on associating cardiovascular disorders with these data. A Discrete Chaotic Fuzzy Neural Network (DC-FNN) model was presented to analyse smart watch functionality for use in fitness and health tracking. This study used machine learning algorithm for analyzing the performance of wearing smart watch embedded device among sports players. The study employs discrete chaotic Fuzzy neural network for evaluating the recognition time and efficiency of the embedded device. The Discrete Chaotic Fuzzy Neural Network (DC-FNN) theories focus on the expertise and experience of specialists who understand how sports system works in different parameters. The major elements of the DC-FNN strategy are based mostly on expert expertise. This research work highlights how wearable sensors can help players and trainers keep tabs on athletes\u27 biomechanical and physiological health in real time, preventing or delaying the start of injuries and providing a more accurate picture of how they are doing. Athlete involvement risk is mediated by the interplay between tissue health and training

Secure Data Collection and Analysis in Smart Health Monitoring

Smart health monitoring uses real-time monitored data to support diagnosis, treatment, and health decision-making in modern smart healthcare systems and benefit our daily life. The accurate health monitoring and prompt transmission of health data are facilitated by the ever-evolving on-body sensors, wireless communication technologies, and wireless sensing techniques. Although the users have witnessed the convenience of smart health monitoring, severe privacy and security concerns on the valuable and sensitive collected data come along with the merit. The data collection, transmission, and analysis are vulnerable to various attacks, e.g., eavesdropping, due to the open nature of wireless media, the resource constraints of sensing devices, and the lack of security protocols. These deficiencies not only make conventional cryptographic methods not applicable in smart health monitoring but also put many obstacles in the path of designing privacy protection mechanisms. In this dissertation, we design dedicated schemes to achieve secure data collection and analysis in smart health monitoring. The first two works propose two robust and secure authentication schemes based on Electrocardiogram (ECG), which outperform traditional user identity authentication schemes in health monitoring, to restrict the access to collected data to legitimate users. To improve the practicality of ECG-based authentication, we address the nonuniformity and sensitivity of ECG signals, as well as the noise contamination issue. The next work investigates an extended authentication goal, denoted as wearable-user pair authentication. It simultaneously authenticates the user identity and device identity to provide further protection. We exploit the uniqueness of the interference between different wireless protocols, which is common in health monitoring due to devices\u27 varying sensing and transmission demands, and design a wearable-user pair authentication scheme based on the interference. However, the harm of this interference is also outstanding. Thus, in the fourth work, we use wireless human activity recognition in health monitoring as an example and analyze how this interference may jeopardize it. We identify a new attack that can produce false recognition result and discuss potential countermeasures against this attack. In the end, we move to a broader scenario and protect the statistics of distributed data reported in mobile crowd sensing, a common practice used in public health monitoring for data collection. We deploy differential privacy to enable the indistinguishability of workers\u27 locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks