Privacy-Preserving Deep Learning: Differentially Private Neural Network Training Against Training-Data Inference

This paper addresses the critical security challenge of protecting sensitive training data in deep neural networks against sophisticated adversarial attacks. We present a comprehensive framework that enables privacy-preserving machine learning while maintaining model utility, focusing on defense mechanisms against white-box adversaries with full access to model parameters and training procedures. Our approach integrates differential privacy into the stochastic gradient descent optimization process through a privacy accounting mechanism that tracks higher-order moments of privacy loss, yielding substantially tighter privacy guarantees compared to traditional composition methods. The framework implements gradient norm clipping to bound individual training examples' influence and adds calibrated Gaussian noise to sanitize parameter updates, creating a provably secure training pipeline. We evaluate our methodology on standard image classification benchmarks, demonstrating that deep neural networks can achieve high accuracy while maintaining strong privacy protection. Experimental results show that models trained under this framework exhibit minimal overfitting, with training and test accuracy gaps remaining small, a direct consequence of the privacy-preserving regularization effect. Systematic parameter sensitivity analysis reveals the interplay between privacy budgets, noise levels, batch configurations, and model architecture choices. The implementation provides a practical privacy-preserving training pipeline built on a widely used machine learning framework, offering organizations a deployable approach to training neural networks on privacy-sensitive datasets with a formal differential privacy guarantee against white-box inference of training records. This research establishes foundational implementation and evaluation techniques for privacy-preserving deep learning applicable to security-critical domains including healthcare, finance, and mobile device applications where training data confidentiality is paramount.