Privacy Preserving Healthcare Based on Secure Mathematical Foundations of Fully Homomorphic Encryption (FHE)

The swift development of digital technology in the healthcare industry has made it crucial to protect sensitive patient data while maintaining its usefulness. One such technique is Homomorphic Encryption (HE), a powerful paradigm that enables computations on encrypted data without the requirement for decryption. This feature ensures that confidential health information remains secure while still allowing for complex analyses and operations. There are three primary categories of HE includes Partially Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE) and Fully Homomorphic Encryption (FHE). The experiments are conducted to demonstrate the effectiveness of HE. In particular, FHE provides the most extensive capabilities by permitting an infinite amount of addition and multiplication operations on ciphertexts. Similarly, FHE ensures semantic security and correctness over other HE techniques based on proper parameter optimization through squashing and bootstrapping. Bootstrapping maintains the noise within safe limit whereas squashing reduces decryption complexity.The performance analysis proves that RLWE based FHE ensures both privacy and utility for healthcare data in computationally efficient way. The privacy-preserving nature of FHE along with its ability to support complex computations and its strong mathematical foundation proving its potential for real-world use cases like secure cloud computing, privacy-preserving machine learning and secure data analytics in healthcare etc.