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<p>Cloud-based healthcare computing has significantly transformed the healthcare landscape. Key advantages include the scalability of services and the flexibility to adjust data capacity as needed. However, this evolution also introduces challenges, such as data breaches, insider threats, phishing attacks, and the use of weak passwords. To overcome these issues, an authentication technique based on blockchain is introduced to ensure the privacy protection of medical data transmitted over the cloud. Here, data authentication is performed by five entities that include patient, cloud server, doctor, Trusted Authority (TA) and blockchain. Then, data augmentation is performed using the processes, such as initialization, registration, key generation. authentication, data protection, data upload, data sharing and data decryption. Further, data is encrypted by the Advanced Encryption Standard (AES) method, which is based on a secret key generated by Deep Maxout Network (DMN). Additionally, KeyGenDMN outperformed the existing technique with a value of 1.533 sec, 41.926 Mb, 3.504 sec, 0.003, 0.770, and 0.535J for computational time, memory usage, latency, throughput, packet loss, reliability metrics, and energy consumption.</p>

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Deep learning and blockchain-based secure authentication protocol for privacy protection in cloud environments

  • 期刊论文

  • 发表日期：

    2025年6月9日

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  • Kalaiselvi Subbarayan,
  • Bindu Garikapati,
  • Sivakumar Chinnavel,
  • Velliangiri Sarveshwaran

摘要

Cloud-based healthcare computing has significantly transformed the healthcare landscape. Key advantages include the scalability of services and the flexibility to adjust data capacity as needed. However, this evolution also introduces challenges, such as data breaches, insider threats, phishing attacks, and the use of weak passwords. To overcome these issues, an authentication technique based on blockchain is introduced to ensure the privacy protection of medical data transmitted over the cloud. Here, data authentication is performed by five entities that include patient, cloud server, doctor, Trusted Authority (TA) and blockchain. Then, data augmentation is performed using the processes, such as initialization, registration, key generation. authentication, data protection, data upload, data sharing and data decryption. Further, data is encrypted by the Advanced Encryption Standard (AES) method, which is based on a secret key generated by Deep Maxout Network (DMN). Additionally, KeyGenDMN outperformed the existing technique with a value of 1.533 sec, 41.926 Mb, 3.504 sec, 0.003, 0.770, and 0.535J for computational time, memory usage, latency, throughput, packet loss, reliability metrics, and energy consumption.