Advanced Privacy-Preserving Framework Using Homomorphic Encryption and Adaptive Privacy Parameters for Scalable Big Data Analysis

Authors

  • R. Shanthi Assistant Professor , Department of computer application, B.S Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Tamil Nadu 600048, India.
  • M. Dinesh Babu Professor, Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chembarambakkam, Chennai , Tamil Nadu 600124
  • N. Kousika Assistant Professor , Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Kuniyamuthur, Tamil Nadu 641008, India.
  • C. Vijayaraj Assistant Professor, Department of Computer Science and Engineering, Kommuri Pratap Reddy Institute of Technology, Medchal, Hyderabad, Telangana State-501301
  • Shruti Bhargava Choubey Associate Professor, Department of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Hyderabad-501301.Telangana,India.
  • S. Sambooranalaxmi Assistant Professor, Department of Electronics and Communication Engineering, P. S. R Engineering College, Sevalpatti, Sivakasi-26140.Tamil Nadu. India.

Keywords:

Big data, Internet of Things (IoT), data mining, privacy-preserving, smart cyber-physical systems, sensor data streams

Abstract

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.

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References

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Published

11.01.2024

How to Cite

Shanthi, R. ., Babu, M. D. ., Kousika, N. ., Vijayaraj, C. ., Choubey, S. B. ., & Sambooranalaxmi, S. . (2024). Advanced Privacy-Preserving Framework Using Homomorphic Encryption and Adaptive Privacy Parameters for Scalable Big Data Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(11s), 160–165. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4433

Issue

Vol. 12 No. 11s (2024)

Section

Research Article

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