This paper studies an acceleration technique for incremental aggregated\ngradient ({\\sf IAG}) method through the use of \\emph{curvature} information for\nsolving strongly convex finite sum optimization problems. These optimization\nproblems of interest arise in large-scale learning applications. Our technique\nutilizes a curvature-aided gradient tracking step to produce accurate gradient\nestimates incrementally using Hessian information. We propose and analyze two\nmethods utilizing the new technique, the curvature-aided IAG ({\\sf CIAG})\nmethod and the accelerated CIAG ({\\sf A-CIAG}) method, which are analogous to\ngradient method and Nesterov's accelerated gradient method, respectively.\nSetting $\\kappa$ to be the condition number of the objective function, we prove\nthe $R$ linear convergence rates of $1 - \\frac{4c_0 \\kappa}{(\\kappa+1)^2}$ for\nthe {\\sf CIAG} method, and $1 - \\sqrt{\\frac{c_1}{2\\kappa}}$ for the {\\sf\nA-CIAG} method, where $c_0,c_1 \\leq 1$ are constants inversely proportional to\nthe distance between the initial point and the optimal solution. When the\ninitial iterate is close to the optimal solution, the $R$ linear convergence\nrates match with the gradient and accelerated gradient method, albeit {\\sf\nCIAG} and {\\sf A-CIAG} operate in an incremental setting with strictly lower\ncomputation complexity. Numerical experiments confirm our findings. The source\ncodes used for this paper can be found on\n\\url{http://github.com/hoitowai/ciag/}.\n