Advanced Privacy-Preserving Framework Using Homomorphic Encryption and Adaptive Privacy Parameters for Scalable Big Data Analysis - Shanthi, R.; Babu, M. Dinesh; Kousika, N.; Vijayaraj, C.; Choubey, Shruti Bhargava; Sambooranalaxmi, S. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
As smart cyber-physical systems advance, they generate substantial valuable data from healthcare, smart homes, and vehicles, often containing sensitive information. This data requires sanitization for safe analysis. However, rapid data generation necessitates scalable privacy-preserving methods with high privacy and utility. Balancing privacy and utility pose a common challenge in preserving data privacy. The Advanced Privacy-Preserving Framework ensures secure data preprocessing for scalable big data analysis. It segments raw data, enabling distributed computation while preserving privacy through homomorphic encryption. Within each segment, normalization and scaling maintain accuracy without compromising privacy. Adaptive privacy parameters and encrypted noise perturbation ensure differential privacy and statistical integrity. Aggregated results remain encrypted until decryption under stringent privacy conditions. Secure data release, compliant with privacy regulations, includes protective measures like random swapping or masking. The Enhanced Privacy-Preserving Data Perturbation Algorithm partitions, encrypts, sorts, perturbs, and securely releases datasets based on a specified threshold. These steps ensure robust privacy and secure data release throughout the analysis.