Quadratic Objective Perturbation: Curvature-Based Differential Privacy
Objective perturbation is a standard mechanism in differentially private empirical risk minimization. In particular, Linear Objective Perturbation (LOP) enforces privacy by adding a random linear term, while strong convexity and stability are ensured by an additional deterministic quadratic term. However, this approach requires the strong assumption of bounded gradients of the loss function, which excludes many modern machine learning models. In this work, we introduce Quadratic Objective Perturbation (QOP), which perturbs the objective with a random quadratic form. This perturbation induces strong convexity and enforces stability of the problem through curvature, thereby enabling privacy...
| Authors | Daniel Cortild, Coralia Cartis |
|---|---|
| Primary category | cs.LG |
| Published | 7 May 2026 |
| Last updated at source | 30 July 2026 |
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