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Mr.Sidharth Sharma
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Abstract : The exponential growth of healthcare data, along with its sensitive nature, has necessitated the development of innovative solutions for protecting patient privacy. Generative AI techniques, such as Generative Adversarial Networks (GANs), have shown promise in creating synthetic healthcare data that mirrors real-world patterns while preserving confidentiality. This paper proposes a privacy-enhanced generative AI framework for the creation of synthetic healthcare data. By incorporating differential privacy and federated learning, the system aims to enhance privacy while maintaining data utility for healthcare research and machine learning tasks. The proposed framework not only safeguards patient information but also enables the creation of diverse, realistic synthetic datasets that can be leveraged for various healthcare applications. Results demonstrate that the synthetic data retains statistical integrity without compromising privacy, making it a viable solution for healthcare data sharing and analysis.
Keyword Privacy-Enhanced Generative AI, Synthetic Healthcare Data, Differential Privacy, Federated Learning, Data Anonymization, Healthcare Data Synthesis, Data Privacy, AI in Healthcare, Privacy-Preserving AI, Synthetic Data Generation.
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Abstract
The rapid expansion of healthcare data, coupled with its sensitive nature, demands advanced methods to safeguard patient privacy. Generative AI—especially Generative Adversarial Networks (GANs)—has emerged as a promising approach for producing synthetic healthcare data that preserves statistical authenticity without exposing personal information. This paper introduces a privacy-enhanced generative AI framework that leverages both differential privacy and federated learning to generate realistic, secure synthetic datasets.
The proposed system ensures data confidentiality while maintaining high utility for healthcare analytics, machine learning, and medical research. It enables privacy-preserving data sharing across institutions and facilitates AI model development without risking patient identity. Experimental results confirm that the synthetic data retains crucial patterns and distributions of the original datasets while meeting strict privacy standards. This framework offers a scalable and secure solution for healthcare data sharing and innovation.
Introduction
In recent years, the healthcare industry has seen a dramatic rise in the collection and use of patient data for applications like disease diagnosis, treatment planning, and clinical research. While this digital transformation brings immense benefits, it also introduces serious concerns regarding data privacy. Medical records often contain highly personal and sensitive information, and traditional data anonymization techniques have proven vulnerable to re-identification threats.Generative AI in Healthcare
As a result, there is a growing need for more secure and reliable methods of protecting patient data while still allowing its use for analysis. Generative AI, particularly Generative Adversarial Networks (GANs), offers a compelling solution. These models can generate synthetic data that closely resembles real datasets but without disclosing identifiable patient details.
This paper presents a novel privacy-enhanced generative AI framework that integrates differential privacy and federated learning to address these challenges. Differential privacy ensures that individual records cannot be reverse-engineered from the synthetic data, while federated learning allows model training across multiple locations without centralizing raw patient data. Together, these technologies enable the creation of high-quality, statistically accurate synthetic datasets suitable for medical AI development, research, and cross-institutional collaboration.
