Adversarial Privacy

Adversarial privacy focuses on protecting sensitive data used in machine learning models from malicious attacks that aim to infer private information. Current research emphasizes developing techniques, often employing adversarial training and differential privacy, to balance the trade-off between model accuracy and privacy preservation across various data types, including text, location data, and speech. This field is crucial for mitigating privacy risks in increasingly data-driven applications, particularly with the rise of powerful large language models and deep learning, and is actively exploring novel metrics and algorithms to quantify and optimize this critical balance.