Privacy Preserving Data Analysis With Generative AI

Abstract

Today, with the involvement of artificial intelligence (AI), where sensitive information is being collected massively to analyse data for various purposes, privacy-preserving data analysis has gained much importance in the data-driven world. Traditionally, using normal data analysis techniques, privacy-preserving methods often faced several security challenges to ensure the privacy of individuals and organizations where the shared data resources have been used for collaboration or decision-making. Hence, generative artificial intelligence has offered numerous solutions that can ensure privacy to such security concerns by enabling privacy-preserving analysis while getting fruitful knowledge from data. Therefore, in this chapter, the authors aim to provide an overview of privacy-preserving data analysis techniques using AI-based approaches such as differential privacy, federated learning (FL), generative adversarial network (GAN), and other variational auto-encoders (VAEs). While providing individuals protection in privacy, they have discussed how generative AI can be used to preserve statistical properties of plain data using synthetic data analysis. Moreover, some real-world applications have also been part of the discussion to illustrate the effectiveness of such techniques in various domains. Finally, they have also provided some future challenges, limitations, and ethical considerations for implementing these techniques effectively and have provided future directions in the rapidly emerging domain.