Type: Master
Status: Available

Wearables facilitate continuous data collection to monitor diverse human behaviors, covering activity, health, and stress. This personal data, including electrocardiogram, movements, and heart rate, is often accessible online for research. Despite the common practice of masking names with random identifiers, studies show that this is insufficient for user identity protection due to the subject-dependent nature of physiological data. This research utilizes existing data and models to implement and assess anonymization techniques such as noise addition and synthetic data generation. The objective is to determine the extent of effective user identity protection while minimizing disruption to human behavior prediction.

This project is available as a MSc thesis or as a BSc thesis (with reduced tasks).

For more information contact: Lidia Alecci, Silvia Santini