Quantum Solutions to the Privacy vs. Utility Tradeoff
This addresses privacy concerns for users of generative models, though it appears incremental as it builds on existing quantum primitives.
The paper tackles the privacy-utility tradeoff in generative models by proposing a quantum cryptographic architecture that provides provable security against membership inference attacks, claiming inherent advantages over Differential Privacy techniques.
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.