The integration of IoT and Ambient Assisted Living (AAL) enables discreet real-time health monitoring in home environments, offering significant potential for personalized and preventative care. However, challenges persist in balancing privacy, cost, usability, and system reliability. This paper provides an overview of recent advancements in sensor and IoT technologies for assisted living, with a focus on elderly individuals living independently. It categorizes sensor types and technologies that enhance healthcare delivery and explores an interdisciplinary framework encompassing sensing, communication, and decision-making systems. Through this analysis, this paper highlights current applications, identifies emerging challenges, and pinpoints critical areas for future research. This paper aims to inform ongoing discourse and advocate for interdisciplinary approaches in system design to address existing trade-offs and optimize performance.

A rising number of Internet of Things (IoT) security and privacy threats have been documented over the last few years. However, IoT devices' domain designs are out-of-date and do not take into consideration the changing dangers associated with them. In this paper, we present COPSEC, a novel framework for evaluating whether IoT devices are compliant with security guidelines and privacy regulations. We extract metrics from existing guidelines and regulations and test them on a set of devices by performing hundreds of automated experiments. Our results indicate not only that these devices are not compliant with basic security guidelines, but also that their data collection operations may introduce privacy risks for the users that adopt them.

Smart TVs implement a unique tracking approach called Automatic Content Recognition (ACR) to profile viewing activity of their users. ACR is a Shazam-like technology that works by periodically capturing the content displayed on a TV's screen and matching it against a content library to detect what content is being displayed at any given point in time. While prior research has investigated third-party tracking in the smart TV ecosystem, it has not looked into second-party ACR tracking that is directly conducted by the smart TV platform. In this work, we conduct a black-box audit of ACR network traffic between ACR clients on the smart TV and ACR servers. We use our auditing approach to systematically investigate whether (1) ACR tracking is agnostic to how a user watches TV (e.g., linear vs. streaming vs. HDMI), (2) privacy controls offered by smart TVs have an impact on ACR tracking, and (3) there are any differences in ACR tracking between the UK and the US. We perform a series of experiments on two major smart TV platforms: Samsung and LG. Our results show that ACR works even when the smart TV is used as a ''dumb'' external display, opting-out stops network traffic to ACR servers, and there are differences in how ACR works across the UK and the US.

"Dictionary of Privacy, Data Protection and Information Security" published on 16 Jul 2024 by Edward Elgar Publishing Limited.