Differential Privacy of Gradient Descent on Perturbed Objectives

arXiv:2610.02716 · cs.LG, cs.CR, stat.ML · Submitted 2026-10-02 · Read on arXiv

cs.LG, cs.CR, stat.ML

Submitted: 2026-10-02

Updated: 2026-10-02

Comments: 50 pages

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Differential Privacy — 1× in this paper · explained in 4 episodes

Sources

• Quadratic Objective Perturbation: Curvature-Based Differential Privacy

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks