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<p>The increased utilization of cloud computing in the health industry simplifies the storage and processing of enormous amounts of confidential data, including electronic health records (EHRs), diagnostic information, and medical images. Despite the easier scaling and accessibility through digitalization, it presents threats in the form of data breaches, misuse, and tampering, which threaten patient confidentiality and institutional reputation. Conventional encryption modes such as 2DES and AES-GCM provide security but come with the cost of high computational expense and latency when employed in real-time for large-scale healthcare data. To eliminate these limitations, A hybrid ChaCha20–Poly1305 encryption method is proposed to secure healthcare data. The system integrates the ChaCha20 stream cipher with the Poly1305 message authentication code to ensure data confidentiality, integrity, and authenticity. The integrated framework provides authenticated encryption and protects against eavesdropping and data tampering. The framework ensures confidentiality and integrity of healthcare data and demonstrates improved performance compared to AES-GCM and 2DES in experimental evaluation. The framework enables secure key generation, high-efficiency encryption, and safe decryption. Experimental implementations on a healthcare dataset demonstrate that the proposed ChaCha20–Poly1305 scheme offers up to 30% more efficient encryption and decryption compared to AES-GCM and 2DES, and sustains throughput of 700–1250 requests per second with latency as low as 30–50 ms. These results confirm that the scheme not only optimizes performance but also maintains privacy, integrity, and scalability. ChaCha20–Poly1305 provides an efficient encryption mechanism for secure healthcare data protection to secure sensitive medical information in simulated healthcare environmens, enabling compliance, trust, and real-time clinical applications. We have not harmed any human person with our research data collection, which was gathered from an already published article.</p>

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A High-Performance ChaCha20–Poly1305 Encryption Scheme for Healthcare Data

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • Dharma Teja Valivarthi,
  • Sreekar Peddi,
  • Sai Sathish Kethu,
  • Modafar Ati,
  • Swapna Narla,
  • Lakshmana Kumar

摘要

The increased utilization of cloud computing in the health industry simplifies the storage and processing of enormous amounts of confidential data, including electronic health records (EHRs), diagnostic information, and medical images. Despite the easier scaling and accessibility through digitalization, it presents threats in the form of data breaches, misuse, and tampering, which threaten patient confidentiality and institutional reputation. Conventional encryption modes such as 2DES and AES-GCM provide security but come with the cost of high computational expense and latency when employed in real-time for large-scale healthcare data. To eliminate these limitations, A hybrid ChaCha20–Poly1305 encryption method is proposed to secure healthcare data. The system integrates the ChaCha20 stream cipher with the Poly1305 message authentication code to ensure data confidentiality, integrity, and authenticity. The integrated framework provides authenticated encryption and protects against eavesdropping and data tampering. The framework ensures confidentiality and integrity of healthcare data and demonstrates improved performance compared to AES-GCM and 2DES in experimental evaluation. The framework enables secure key generation, high-efficiency encryption, and safe decryption. Experimental implementations on a healthcare dataset demonstrate that the proposed ChaCha20–Poly1305 scheme offers up to 30% more efficient encryption and decryption compared to AES-GCM and 2DES, and sustains throughput of 700–1250 requests per second with latency as low as 30–50 ms. These results confirm that the scheme not only optimizes performance but also maintains privacy, integrity, and scalability. ChaCha20–Poly1305 provides an efficient encryption mechanism for secure healthcare data protection to secure sensitive medical information in simulated healthcare environmens, enabling compliance, trust, and real-time clinical applications. We have not harmed any human person with our research data collection, which was gathered from an already published article.