In this paper, we propose a differentially private decentralized learning method (termed PrivSGP-VR) which employs stochastic gradient push with variance reduction and guarantees (epsilon, delta)-differential privacy (DP) for each node. Our theoretical analysis shows that, under DP Gaussian noise with constant variance, PrivSGP-VR achieves a sub-linear convergence rate of O(1/sqrt(nK)), where n and K are the number of nodes and iterations, respectively, which is independent of stochastic gradient variance, and achieves a linear speedup with respect to n. Leveraging the moments accountant method, we further derive an optimal K to maximize the model utility under certain privacy budget in decentralized settings. With this optimized K, PrivSGP-VR achieves a tight utility bound of O(sqrt(d*log(1/delta))/(sqrt(n)*J*epsilon)), where J and d are the number of local samples and the dimension of decision variable, respectively, which matches that of the server-client distributed counterparts, and exhibits an extra factor of 1/sqrt(n) improvement compared to that of the existing decentralized counterparts, such as A(DP)2SGD. Extensive experiments corroborate our theoretical findings, especially in terms of the maximized utility with optimized K, in fully decentralized settings.