  Due to the computational cost of running inference for a neural network, the
need to deploy the inferential steps on a third party's compute environment or
hardware is common. If the third party is not fully trusted, it is desirable to
obfuscate the nature of the inputs and outputs, so that the third party can not
easily determine what specific task is being performed. Provably secure
protocols for leveraging an untrusted party exist but are too computational
demanding to run in practice. We instead explore a different strategy of fast,
heuristic security that we call Connectionist Symbolic Pseudo Secrets. By
leveraging Holographic Reduced Representations (HRR), we create a neural
network with a pseudo-encryption style defense that empirically shows
robustness to attack, even under threat models that unrealistically favor the
adversary.
