Data Obfuscation

Data obfuscation techniques aim to protect sensitive information within datasets while preserving data utility for analysis or machine learning. Current research focuses on developing methods that balance privacy and utility, employing diverse approaches such as context-sensitive obfuscation using transformer models (like BERT) for structured data, backpropagation refinement for image obfuscation, and matrix masking within trusted execution environments for deep learning. These advancements are crucial for addressing privacy concerns in data sharing, AI model training, and various applications where sensitive data needs protection, impacting fields ranging from healthcare to finance.