Balancing privacy and utility of smart devices utilizing explicit and implicit context

The swift evolution of communication technologies, coupled with advancements in sensors and machine learning, has significantly accelerated the pervasive integration of smart Internet of Things (IoT) devices into various aspects of our daily lives. Examples range from automating homes to optimizing industrial processes and improving healthcare. While these applications enhance quality of life and operational efficiency, they also raise concerns about user privacy due to the collection and processing of personal data. Ensuring the seamless and secure integration of these technologies is crucial. Balancing the benefits of smart applications with protecting user privacy is the key challenge. To address this issue, we present a general method as well as customized approaches for specific scenarios. The general method involves data synthesis, which safeguards privacy by substituting real data with synthetic data. We propose an unsupervised statistical feature-guided diffusion model (SF-DM) for sensor data synthesis. SF-DM generates diverse and representative synthetic sensor data without the need for labeled data. Specifically, statistical features such as mean, standard deviation, Z-score, and skewness are introduced to guide the sensor data generation. Regarding customized approaches for specific scenarios, we address both active (explicit context) and passive (implicit context) situations. Explicit context typically includes information willingly shared while implicit context may encompass data collected passively, with users potentially unaware of the full extent of information usage. Segregating explicit and implicit context aims for a balance between personalization and privacy, empowering users with enhanced control over their information and ensuring adherence to privacy regulations. In active scenarios, we focus on privacy protection in pervasive surveillance. We propose Point-Former, the example-guided modification of motion in point cloud to translate from default motion and gesture interaction alphabets to personal ones, to safeguard privacy during gesture interactions in pervasive space. In the passive scenario involving implicit context, we consider on-body devices and environmental devices. For on-body devices, we introduce \textbf{CardioID}, an interaction-free device pairing method that generates body-implicit secure keys by exploiting the randomness in the heart's operation (electrocardiogram (ECG) or ballistocardiogram (BCG) signals). For environmental smart devices, we propose GIHNET, a low complexity and secure GAN-based information hiding method for IoT communication via an insecure channel. It hides the original information using meaningless representations, by obscuring it beyond recognition. Building on GIHNET, we extend the use of data encryption and propose SIGN, which converts signatures into a Hanko pattern and uses it as an encryption method to generate digital signatures in pervasive spaces.