A Comprehensive Review of Differential Privacy Optimization

Differential Privacy (DP) is a technique designed to protect individualprivacy in data analysis and machine learning. It ensures that theinclusion or exclusion of any single data point doesn’t significantlychange the overall results. As we increasingly collect and analyzesensitive data, it’s becoming crucial to balance keeping data functionaland protecting privacy. This paper reviews the key ideas, methods, andrecent developments in differential privacy optimization. We explorethe basics of DP techniques, like the Laplace and Gaussianmechanisms, and how they’re used in optimization algorithms such asdifferentially private stochastic gradient descent (DP-SGD). We alsoexamine how DP is applied across various machine learning areas,including supervised, deep, and federated learning.Additionally, we discuss the challenges of maintaining privacy withoutsacrificing scalability and accuracy. Finally, we consider open researchquestions and future directions, such as adaptive DP mechanisms andthe application of DP in emerging fields like quantum computing,synthetic data generation, and the Internet of Things (IoT). This reviewaims to be a helpful resource for researchers and practitioners, clearlyunderstanding the current state of DP optimization and offering insightsinto future innovations.

Keywords:

Differential Privacy , Optimization , Data Privacy , Machine Learning , Federated Learning , Privacy-Preserving Algorithms , Internet of Things , Quantum Computing.