  Control related data, such as system states and inputs or controller
specifications, is often sensitive. Meanwhile, the increasing connectivity of
cloud-based or networked control results in vast amounts of such data, which
poses a privacy threat, especially when evaluation on external platforms is
considered. In this context, a cipher based on a random affine transformation
gained attention, which is supposed to enable privacy-preserving evaluations of
quadratic programs (QPs) with little computational overhead compared to other
methods.
  This paper deals with the security of such randomly transformed QPs in the
context of model predictive control (MPC). In particular, we show how to
construct attacks against this cipher and thereby underpin concerns regarding
its security in a practical setting. To this end, we exploit invariants under
the transformations and common specifications of MPC-related QPs. Our numerical
examples then illustrate that these two ingredients suffice to extract
information from ciphertexts.
