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Search for the optimizer in computationally demanding model predictivecontrol (MPC) setups can be facilitated by Cloud as a service provider incyber-physical systems. This advantage introduces the risk that Cloud canobtain unauthorized access to the privacy-sensitive parameters of the systemand cost function. To solve this issue, i.e., preventing Cloud from accessingthe parameters while benefiting from Cloud computation, random affinetransformations provide an exact yet light weight in computation solution. Thisresearch deals with analyzing privacy preserving properties of thesetransformations when they are adopted for MPC problems. We consider two commonstrategies for outsourcing the optimization required in MPC problems, namelyseparate and dense forms, and establish that random affine transformationsutilized in these forms are vulnerable to side-knowledge from Cloud.Specifically, we prove that the privacy guarantees of these methods and theirextensions for separate form are undermined when a mild side-knowledge aboutthe problem in terms of structure of MPC cost function is available. Inaddition, while we prove that outsourcing the MPC problem in the dense forminherently leads to some degree of privacy for the system and cost functionparameters, we also establish that affine transformations applied to this formare nevertheless prone to be undermined by a Cloud with mild side-knowledge.Numerical simulations confirm our results.

Privacy Analysis of Affine Transformations in Cloud-based MPC: Vulnerability to Side-knowledge