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