The field of sensing and monitoring is moving towards a greater emphasis on privacy and security. Researchers are developing innovative methods to protect sensitive data, such as gaze signals and personal communications, while still allowing for useful analysis and feedback. One notable trend is the use of noise-infused autoencoders and other machine learning techniques to prevent re-identification and maintain data utility. Another area of focus is the development of privacy-preserving sensing systems, such as those using ultra-wideband devices or low-cost sensors, to monitor posture, detect plant stress, and prevent disease in livestock. These advancements have the potential to improve health outcomes, enhance quality of life, and increase efficiency in various industries. Noteworthy papers include: Privacy Enhancement for Gaze Data Using a Noise-Infused Autoencoder, which presents a novel approach to protecting gaze data, and UWB-PostureGuard, which demonstrates a highly accurate and scalable system for monitoring ergonomic sitting posture. Additionally, iTrace introduces a click-based gaze visualization method that overcomes privacy restrictions on the Apple Vision Pro, and Supporting Socially Constrained Private Communications with SecureWhispers proposes a method for strictly private communication between devices without relying on third-party infrastructure.