Evaluation of Privacy-Preserving Techniques: Bouncy Castle Encryption and Machine Learning Algorithms for Secure Classification of Sensitive Data

Authors

  • Tungar D. V. Department of Computer Engineering, MET's Institute of Engineering, Bhujbal Knowledge City, Adagaon, Nashik (Maharashtra), India, Savitribai Phule Pune University, Pune (Maharashtra), India
  • Patil D. V. Department of Computer Engineering, Gokhale Education Society's R.H. Sapat College of Engineering, Management Studies and Research, Nashik (Maharashtra), India

Keywords:

Machine learning, Data encryption, privacy, data privacy, sensitive information security, data security, RSA-2048 algorithm, encryption, predictive accuracy

Abstract

In the present era of data-driven society, where the protection of sensitive information is of utmost importance, this research paper aims to enhance the effectiveness of data encryption and compare it with machine learning models (ML). This research examines different privacy-preserving methods using the machine learning algorithms and encryption library of Bouncy Castle for securely classifying sensitive data. To encrypt the dataset, the study utilizes the Bouncy Castle providing encryption by RSA-2048 algorithm. The method uses lookup substitution with k-anonymization, which reduces data risks, to improve privacy protection and system performance. This method successfully substitutes sensitive data with anonymized values, protecting privacy while enhancing system efficiency. To implement privacy preservation techniques, a dataset containing employee details is employed, and several classifiers including Adaboost M1, Naïve Bayes, Decision Tree J48, Random Forest and Decision Tree ID3, are utilized. The effectiveness of these algorithms is determined by evaluating their performance using significant metrics. The outcomes show that the Decision Tree J48 method surpasses others in terms of classification performance. Furthermore, the study evaluates the encryption process's efficiency and effectiveness by assessing processing time, encryption time, and decryption time. This investigation gives light on the technique's impact on data security and time overhead. The research's conclusions offer insightful information about the value of data encryption and help decision-makers make sensible choices when selecting appropriate security measures for various use cases. These observations help us comprehend the importance of data encryption and how it helps to protect the security and privacy of that data.

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Published

01.07.2023

How to Cite

D. V. , T. ., & D. V., P. . (2023). Evaluation of Privacy-Preserving Techniques: Bouncy Castle Encryption and Machine Learning Algorithms for Secure Classification of Sensitive Data. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 429 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2976

Issue

Vol. 11 No. 7s (2023): Advancements in Machine Learning for Computer Science and Decision Support Systems

Section

Research Article

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