  In this work, we propose a novel architecture (and several variants thereof)
based on quantum cryptographic primitives with provable privacy and security
guarantees regarding membership inference attacks on generative models. Our
architecture can be used on top of any existing classical or quantum generative
models. We argue that the use of quantum gates associated with unitary
operators provides inherent advantages compared to standard Differential
Privacy based techniques for establishing guaranteed security from all
polynomial-time adversaries.

Quantum Physics;Cryptography and Security;Machine Learning