Public defence in Networking Technology, M.Sc. Si Zuo

The title of the thesis: Balancing privacy and utility of smart devices utilizing explicit and implicit context

Thesis defender: Si Zuo
Opponent: Prof. Eran Toch, Tel Aviv University, Israel
Custos: Prof. Stephan Sigg, Aalto University School of Electrical Engineering, Department of Information and Communications Engineering 

The thesis explores solutions to balance the benefits of smart devices with the protection of user privacy. 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. Specifically, 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. Statistical features such as mean, standard deviation, Z-score, and skewness are used 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 its usage. Segregating explicit and implicit contexts aims to balance 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, an example-guided modification method for motion in point clouds. Point-Former translates default motion and gesture interaction patterns into personalized ones, protecting privacy during gesture interactions in pervasive spaces. In the passive scenario involving implicit context, we consider on-body devices and environmental devices. For on-body devices, we introduce CardioID, an interaction-free device pairing method that generates body-implicit secure keys by exploiting the randomness in heart activity. For environmental smart devices, we propose GIHNET, a low-complexity and secure GAN-based information hiding method for IoT communication via insecure channels. GIHNET obscures original information beyond recognition by hiding it within meaningless representations. 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.

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/

Doctoral theses in the School of Electrical Engineering: https://aaltodoc.aalto.fi/handle/123456789/53