Trade-offs Between Privacy and Performance in Encrypted Dataset using Machine Learning Models

Abstract

In recent years, with the increasing importance of dataset privacy in machine learning (ML) applications, there has been an increased demand for secure and privacy-preserving solutions. Consequently, encryption techniques have become known as a critical tool for protecting data privacy in an era of massive data use, exchange, and analysis. Encryption protects data against illegal access and disclosure by changing it into unreadable ciphertext that can only be decrypted by authorized parties. In the field of ML, where sensitive data is often utilized, in such a process the use of encryption techniques has significant potential for providing privacy-preserving model training and inference. Therefore, this article analyzes, investigates, and compares three widely used encryption techniques. Each encryption method offers unique advantages and trade-offs. Thus, we evaluate the performance of Convolutional Neural Network (CNN) models trained on encrypted datasets using these encryption techniques to provide detailed information on the effectiveness, practical concerns, and applicability of various methods for real-world applications by completely analyzing them within the context of computer vision. We test the performance of CNN models trained on encrypted data with several encryption approaches using neural models based-architecture. Parameters such as training time, memory usage, and classification accuracy are analyzed and compared between encryption methods. We also look into the effect of encryption on model interpretability and robustness against adversarial attacks. Furthermore, to support our study we demonstrate our approach by using practical implementation—to showcase the performance and efficiency of each encryption strategy in protecting data privacy while keeping model accuracy and testing in a real-time recognition application using an edge device such as NVIDIA Jetson. Through this comparative analysis, researchers and developers can achieve a more in-depth understanding of the importance and issues involved with the integration of encryption techniques into ML especially in computer vision application workflows.