Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant (2201.10838v10)

Abstract: Training logistic regression over encrypted data has been a compelling approach in addressing security concerns for several years. In this paper, we introduce an efficient gradient variant, called $quadratic$ $gradient$, for privacy-preserving logistic regression training. We enhance Nesterov's Accelerated Gradient (NAG), Adaptive Gradient Algorithm (Adagrad) and Adam algorithms by incorporating their quadratic gradients and evaluate these improved algorithms on various datasets. Experimental results demonstrate that the enhanced algorithms achieve significantly improved convergence speed compared to traditional first-order gradient methods. Moreover, we applied the enhanced NAG method to implement homomorphic logistic regression training, achieving comparable results within just 4 iterations. There is a good chance that the quadratic gradient approach could integrate first-order gradient descent/ascent algorithms with the second-order Newton-Raphson methods, and that it could be applied to a wide range of numerical optimization problems.