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Despite the increasing relevance of private transport operators as Mobility-as-a-Service in the success of smart cities, desire for privacy in data sharing limits collaborations with public agencies. We propose an original model that circumvents this limitation, by designing a diffusion of the data - in this case, service tour data - such that passenger travel times remain reliable to the recipient agency. The Tour Sharing Privacy Design Problem is formulated as a nonlinear programming problem that maximizes entropy. We investigate properties of the model and iterative tour generation algorithms, and conduct a series of numerical experiments on an instance that has 90 feasible tours. The experimental results show that a k-best shortest tour approach of generating tours iteratively initially increases the gap to a lower bound before decreasing toward a final constraint gap. The model is shown to recognize the trade-offs between more reliability in data and more anonymity. Comparisons between the true and diffused travel times and OD matrices are made.