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<p>Dermatology increasingly relies on large-scale, high-resolution imaging data for the diagnosis and management of skin diseases. Cloud computing offers a flexible, scalable, and cost-effective infrastructure to meet these computational and data-intensive demands. By enabling the deployment of deep learning models and teledermatology services, cloud platforms support rapid analysis of skin lesion images, remote consultations, and global research collaboration, thereby enhancing efficiency and expanding access to care. Despite these benefits, the integration of cloud technologies introduces critical challenges in privacy, security, and regulatory compliance. Sensitive imaging data often contain personally identifiable information, making them vulnerable to breaches and misuse if not properly safeguarded. Addressing these risks is essential for maintaining patient trust and meeting requirements under Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union, and similar frameworks. This review provides a comprehensive overview of cloud-based dermatological imaging with a focus on privacy protection. We examine key applications, identify major challenges and remedies such as data security, latency, and governance, and highlight emerging solutions including hybrid edge–cloud models, blockchain integration, privacy-preserving artificial intelligence, the Internet of Things-Driven Skin Detection device and synthetic data generation. In particular, we discuss the emerging role of hybrid quantum–classical computing (HQCC) as a cloud-native paradigm that addresses both computational scalability and security constraints. By offloading optimization-intensive tasks to cloud-accessible quantum processors while retaining sensitive image data at local or edge nodes, HQCC enables quantum-accelerated analysis without exposing raw medical images. Furthermore, the integration of quantum cryptography, such as quantum key distribution, provides information-theoretic security for federated learning and inter-institutional communication.</p>

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Privacy-preserving cloud-based dermatological image processing for medical applications: a review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月18日

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  • Siyan Chen,
  • Xueer Zhang,
  • E. Liu,
  • Yanjiao Xiong,
  • Lin Wang,
  • Xiaozhe Gu,
  • Zhehui Wang,
  • Jing Guo,
  • Tao Luo

摘要

Dermatology increasingly relies on large-scale, high-resolution imaging data for the diagnosis and management of skin diseases. Cloud computing offers a flexible, scalable, and cost-effective infrastructure to meet these computational and data-intensive demands. By enabling the deployment of deep learning models and teledermatology services, cloud platforms support rapid analysis of skin lesion images, remote consultations, and global research collaboration, thereby enhancing efficiency and expanding access to care. Despite these benefits, the integration of cloud technologies introduces critical challenges in privacy, security, and regulatory compliance. Sensitive imaging data often contain personally identifiable information, making them vulnerable to breaches and misuse if not properly safeguarded. Addressing these risks is essential for maintaining patient trust and meeting requirements under Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union, and similar frameworks. This review provides a comprehensive overview of cloud-based dermatological imaging with a focus on privacy protection. We examine key applications, identify major challenges and remedies such as data security, latency, and governance, and highlight emerging solutions including hybrid edge–cloud models, blockchain integration, privacy-preserving artificial intelligence, the Internet of Things-Driven Skin Detection device and synthetic data generation. In particular, we discuss the emerging role of hybrid quantum–classical computing (HQCC) as a cloud-native paradigm that addresses both computational scalability and security constraints. By offloading optimization-intensive tasks to cloud-accessible quantum processors while retaining sensitive image data at local or edge nodes, HQCC enables quantum-accelerated analysis without exposing raw medical images. Furthermore, the integration of quantum cryptography, such as quantum key distribution, provides information-theoretic security for federated learning and inter-institutional communication.