PSV-GWO: Particle Swarm Velocity Aided GWO for Privacy Preservation of Data

Abstract

Due to the maximum usage of Social Networking Sites (SNS) the number of individuals that are posting their health information online is increasing. The health information of the user’sis disclosed on these sites, where the organization or various individuals can mine that for numerous research and commercial purposes. Because of this sensitive nature of the medical information, the privacy protection is said to be a main focus for the researchers. On analyzing many of the conventional methods, there is an improvement in the sanitization process but still lacks on the restoration of data. Thus, this paper focused on the privacy preservation over the healthcare records. The proposed model is about the enhancement in the sanitization technique that hides the raw information presented by the users. The sanitization process involves the generation of key that created optimally by introducing a new Particle Swarm Velocity aided GWO (PSV-GWO) algorithm. Additionally, the authorized user can restore these sanitized medical data securely. Finally, the traditional algorithms are compared with the proposed model in terms of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Crow Search Optimization (CSA) and Adaptive Awareness Probability-based CSA (AAP-CSA) and the outcome is analyzed.

Bio inspired based Multi-objective Data-Privacy-Preservation-Technique (MO-DPPT) system model for cloud data

“…Despite that, if more users use the data pool, it is very tough to keep the privacy of the database. Jyothi Mandala (2019) [22] has proposed the Particle Swarm Velocity Aided GWO (PSV-GWO) technique for Privacy Preservation of Data. The main strategy in this proposed model uses an improved sanitization process in which, the original data given by the users is concealed.…”

“…According to the current locations of all individual fishes, the centroid G is determined and the Equation ( 21) represents its corresponding weight. (21) Using Equation (22), the current position of all individual fishes is updated, when there is a rise in swarm size during current looping operations. Alternately, when there is a drop in swarm size during current looping operations, using Equation ( 23), the current position of all individual fishes is updated.…”