A comparative Study on AI-Driven Anonymization Techniques for Protecting Personal Data

In the Artificial Intelligence era, protecting individual users' data has become crucial. The collected data is stored in multiple databases having personally identifiable information (PII). This may provide a significant privacy concern for the database. Several privacy-preserving approaches have been proposed, including Differential Privacy, Homomorphic Encryption, Generative Adversarial Network and Federated Learning. In this paper, the above four anonymization techniques are compared. In addition, this study will review the strengths and weaknesses of these techniques. We also discuss the trade-off between data utility and privacy. The results of this study aim to guide researchers and practitioners in selecting suitable AI-driven anonymization techniques.