We currently explore the application of haptic augmentation in the context of palpation training systems. The key idea is to modify real touch sensations with computed haptic feedback. In earlier work, we have introduced an algorithmic framework for determining appropriate augmentation forces during interaction at one contact point. In this paper, we present an extension of the approach to deal with manipulations at more than one contact location. At the heart of our method is the data-driven estimation of Hunt-Crossley model parameters in a pre-computation step. Feeding the parameters into a contact dynamics model allows us to approximate the feedback behavior of various physical tissue mock-ups. Further, we combine the parameter estimation with the tracking of the position of a stiffer inclusion in the mock-up. These data are employed to create a model of movement due to external forces. The combination of these models then allows us to represent and render the mutual effects at multiple contact points. Several experiments have been carried out on a setup with two haptic devices. Comparisons of recorded with simulated interaction data demonstrate the performance and potential of our method.