Local Selfie: A Web-Based Application for Digital Footprint Visualization and Privacy Awareness

Abstract

The rapid growth of digital systems has increased concerns regarding user privacy, security, and transparency. Local Selfie is a web-based application designed to monitor and visualize a user’s local digital footprint in real time. It tracks system parameters such as CPU usage, memory consumption, processes, file activities, and network connections to provide comprehensive insights into system behavior. The system integrates machine learning using the Isolation Forest algorithm to detect anomalies, along with behavioral analysis to identify deviations from normal patterns. AI-generated explanations enhance user understanding of potential threats. All data is stored locally using SQLite, ensuring privacy. The platform delivers real-time analytics and intuitive dashboards, enabling users to effectively monitor and secure their computing environment.