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Pairwise learning focuses on learning tasks with pairwise loss functions,depends on pairs of training instances, and naturally fits for modelingrelationships between pairs of samples. In this paper, we focus on the privacyof pairwise learning and propose a new differential privacy paradigm forpairwise learning, based on gradient perturbation. Except for the privacyguarantees, we also analyze the excess population risk and give correspondingbounds under both expectation and high probability conditions. We use the\textit{on-average stability} and the \textit{pairwise locally elasticstability} theories to analyze the expectation bound and the high probabilitybound, respectively. Moreover, our analyzed utility bounds do not requireconvex pairwise loss functions, which means that our method is general to bothconvex and non-convex conditions. Under these circumstances, the utility boundsare similar to (or better than) previous bounds under convexity or stronglyconvexity assumption, which are attractive results.

Towards Sharper Utility Bounds for Differentially Private Pairwise Learning