How to Disappear Online
Abstract
In the digital age, personal data privacy has become a critical concern, driven by the proliferation of online platforms and the increasing volume of data storage. This thesis explores the challenges and methodologies surrounding digital erasure, focusing on compliance with privacy regulations such as General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and the Artificial Intelligence Act (AIA).
The research investigates machine learning techniques, particularly Random Forest and Decision Tree classifiers, to track, match, and prepare personal data for deletion across complex systems. By adapting intrusion recovery methodologies, this thesis proposes a system for accurately identifying and managing data marked for deletion, ensuring organizations meet regulatory obligations. The analysis demonstrates how machine learning models enhance data matching, focusing specifically on log identification as a crucial step in the broader process of data deletion.
This work contributes to the broader discourse on data privacy by addressing the practical challenges of data management in distributed environments, paving the way for a more secure and compliant digital erasure framework.
Keywords
Data Privacy, Digital Erasure, GDPR, CCPA, AI Regulation, Intrusion Recovery, Machine Learning, Security
This Master’s thesis presents a comprehensive approach to secure data deletion, blending machine learning, regulatory compliance, and intrusion recovery techniques to create a framework for digital privacy and data protection. 🚀