ElGamal Based Homophorphic Encryption UsingAT-BiGRU for Efficient Privacy PreservingDisease Prediction Scheme in Healthcare Data

Abstract

Internet of Things (IoT) has revolutionized mobile healthcare applications, increases diagnosis speed andaccuracy. Disease prediction systems (DPS) improve healthcare quality, but they raise privacy concerns due to sensitivedata. These concerns include illegal sharing, misuse, and exposure of sensitive information. However, developing newtechniques does not provide improved protection from attackers and fraudsters. This paper suggests an effectiveprivacy-preserving strategy for patient healthcare data from IoT devices in order to anticipate diseases in thecontemporary medical field. Heart failure prediction health data is utilized as an input in this proposed approach.Initially, elastic net (EN) is used to reduce the dimensionality of the input raw dataset. Hyper parameter in EN isoptimally selected using the Zebra Optimization Algorithm (ZOA). After dimensionality reduction, data is encryptedusing the ElGamal technique. During the encryption procedure, the True Random Number Generator-Pseudo RandomNumber Generator (TRNG-PRNG) encryption method is used to generate the secret key. These encrypted data issecurely stored in cloud. Finally, Attention Mechanism based Bi-directional Gated Recurrent Unit (AT-BiGRU)technique is employed to predict heart disease. The ElGamal-TRPRNG method strengths privacy and security byachieving encryption and decryption times of 0.30 sec and 0.12 sec, respectively. The suggested model is evaluated andcontrasted with current methods using encryption data performance measures. Model achieved 93.47% accuracy, 6.53%error, and 93.45% precision in encrypted data performance metrics. Therefore, this suggested method is the mosteffective way to effectively safeguard health care records.