Addressing Data Privacy and Security Challenges in Telemedicine Applications Utilizing Convolutional Neural Networks

EasyChair Preprint 14658

Abstract

Telemedicine has rapidly evolved as a critical component of modern healthcare, enabling remote diagnosis and treatment through digital platforms. Convolutional Neural Networks (CNNs) play a pivotal role in automating the analysis of medical images, facilitating real-time disease diagnosis. However, the integration of CNNs into telemedicine applications raises significant concerns regarding data privacy and security. This abstract explores the challenges and potential solutions associated with ensuring the confidentiality, integrity, and availability of sensitive medical data in CNN-powered telemedicine systems.

The research begins by outlining the specific data privacy risks in telemedicine, such as unauthorized access, data breaches, and patient identity theft. It highlights the vulnerabilities introduced by the use of CNNs, including potential adversarial attacks that could manipulate model predictions or expose sensitive patient information. The study also addresses the complexities of securing data at various stages of the CNN pipeline, from data acquisition and transmission to storage and processing.

To mitigate these challenges, the research investigates several privacy-preserving techniques, including data encryption, anonymization, and the use of secure multi-party computation (SMPC) and homomorphic encryption. It also explores the application of differential privacy to protect patient data during CNN model training, ensuring that the inclusion or exclusion of a single patient's data does not significantly affect the model's output.

Keyphrases: Convolutional Neural Networks (CNNs), Data Security, Encryption, Federated Learning, GDPR, HIPAA, Healthcare AI, Privacy-preserving techniques, Regulatory Compliance, Secure Telemedicine, Telemedicine, adversarial attacks, data privacy, differential privacy

@booklet{EasyChair:14658,
  author    = {Dylan Stilinki},
  title     = {Addressing Data Privacy and Security Challenges in Telemedicine Applications Utilizing Convolutional Neural Networks},
  howpublished = {EasyChair Preprint 14658},
  year      = {EasyChair, 2024}}