Anonymised Data

Anonymized data aims to protect individual privacy while preserving data utility for research and applications. Current research focuses on developing and evaluating techniques like differential privacy, k-anonymity, pseudonymization, and synthetic data generation, often employing machine learning models (including LLMs) to achieve a balance between privacy and data utility. This field is crucial for responsible data usage, enabling advancements in areas like federated learning and personalized recommendations while mitigating re-identification risks and complying with regulations like GDPR. The ongoing challenge lies in finding optimal anonymization methods that minimize information loss while effectively safeguarding sensitive information.