Noise-Resilient Homomorphic Encryption: A Framework for Secure Data Processing in Health care Domain

TL;DR

The paper addresses secure processing of sensitive healthcare data using fully homomorphic encryption by proposing the Fully Homomorphic Integrity Model (HIM), which targets noise resilience, efficiency, and data integrity. HIM combines noise-management via rational-number adjustment, personalized prime-based key generation, bootstrapping, and a robust decryption mechanism to enable accurate computations on encrypted healthcare data. The authors provide a detailed framework, including key generation, encryption, evaluation, bootstrapping, and decryption, plus a matrix-based illustrative example and complexity analysis. Experimental results show that the proposed method achieves fast encryption (≈35 ms), rapid decryption (≈140 ms), and compact ciphertext (≈4 KB) with low noise growth, indicating practical applicability for real-time healthcare analytics and privacy-preserving data processing.

Abstract

In this paper, we introduce the Fully Homomorphic Integrity Model (HIM), a novel approach designed to enhance security, efficiency, and reliability in encrypted data processing, primarily within the health care industry. HIM addresses the key challenges that noise accumulation, computational overheads, and data integrity pose during homomorphic operations. Our contribution of HIM: advances in noise management through the rational number adjustment; key generation based on personalized prime numbers; and time complexity analysis details for key operations. In HIM, some additional mechanisms were introduced, including robust mechanisms of decryption. Indeed, the decryption mechanism ensures that the data recovered upon doing complex homomorphic computation will be valid and reliable. The healthcare id model is tested, and it supports real-time processing of data with privacy maintained concerning patients. It supports analytics and decision-making processes without any compromise on the integrity of information concerning patients. Output HIM promotes the efficiency of encryption to a greater extent as it reduces the encryption time up to 35ms and decryption time up to 140ms, which is better when compared to other models in the existence. Ciphertext size also becomes the smallest one, which is 4KB. Our experiments confirm that HIM is indeed a very efficient and secure privacy-preserving solution for healthcare applications

Figures (5)

• Figure 1: Recovered data.
• Figure 2: Execution time.
• Figure 3: Comparison of fully homomorphic encryption methods.
• Figure 4: Comparison of fully homomorphic encryption methods.
• Figure 5: Comparison of Time taken for encryption and decryption.

Theorems & Definitions (1)

• Theorem 3.1