Data Privacy

Data privacy research focuses on developing methods to protect sensitive information during data collection, processing, and analysis, particularly within machine learning applications. Current research emphasizes techniques like differential privacy, federated learning, and homomorphic encryption, often implemented using neural networks, large language models, and generative adversarial networks, to mitigate privacy risks while maintaining data utility. This field is crucial for responsible AI development, impacting various sectors by enabling privacy-preserving data sharing and analysis in healthcare, finance, and other sensitive domains. The ongoing development of robust privacy-preserving techniques is essential for building trust and ensuring ethical use of data-driven technologies.