Exploring Deep Learning Approaches for Real-Time Anonymization and Privacy Preservation in Unstructured Data

In an era dominated by Big Data, safeguarding privacy across unstructured data such as text, image, and audio datasets presents unique complexities which can be solved using AI-powered anonymization techniques. In this survey, we explore state-of-the-art approaches for text processing such as Named Entity Recognition (NER) systems, underpinned by deep learning architectures like Recurrent Neural Networks (RNNs) (e.g., LSTMs, GRUs) and Transformer Models. For image and audio, our study explores specific methods for object recognition and redaction techniques, acknowledging the intricate specific challenges of these data types. We also scrutinize real-time anonymization approaches, including masking, token substitution, and random perturbation, aiming to minimize latency while preserving the inherent value of data. Conversely, beyond anonymization, our study explores the integration of cryptographic safeguards, including homomorphic encryption and differential privacy to strengthen personal data repositories against re-identification queries. Finally, this survey also identifies privacy-preserving access control, which is a crucial requirement for real-time anonymization mechanisms so that unauthorized access to heterogenous data sources can be effectively mitigated. Our contribution is to identify key areas for future research regarding the application of both advanced deep learning approaches, and anonymization techniques in order to facilitate the processing of unstructured data for preserving user privacy while ensuring acceptable real-time performance.