Privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications

TL;DR

This review comprehensively discusses privacy-preserving techniques like encryption, federated learning, and differential privacy in medical image analysis, highlighting their applications, challenges, and future research directions to protect sensitive patient data.

Contribution

It systematically organizes privacy-preserving methods in medical image analysis, linking technical solutions to practical challenges and identifying research gaps and emerging trends.

Findings

Encryption and federated learning are effective in protecting patient privacy.

Differential privacy and homomorphic encryption enable secure data sharing.

Emerging techniques like zero-knowledge proofs show promise for future applications.

Abstract

With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis.