Privacy preservation of data using crow search with adaptive awareness probability

Abstract Information security and privacy in the healthcare sector is a concern of increasing significance. The adoption of increased regulation, digital patient records, provider consolidation, and the growing requirement for information among patients, payers, and providers, all point towards the necessitation for improved information security. In most of the traditional anonymization methods, it is more robust on data sanitization process, yet not on data restoration. Even though few privacy preservation algorithms have been introduced recently, the accuracy of sanitization seems to be low. Hence, this paper intends to develop enhanced medical data preservation in healthcare data. The proposed scheme mainly concerns on setting up an effective sanitizing approach for hiding the sensitive rules offered by clients. For sanitizing the medical data, a secure key is generated which optimally selected using improved Crow Search Algorithm (CSA) known as Adaptive Awareness Probability-based CSA (AAP-CSA). In addition, the sanitized medical data is restored securely by the authorized user. Moreover, the proposed scheme is compared with other conventional algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and Crow Search Optimization (CSA) and the results are obtained.