Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems
This provides a more flexible privacy framework for quantum computing systems, though it appears to be an incremental extension of classical pufferfish privacy to the quantum domain.
The authors proposed quantum pufferfish privacy (QPP), a flexible privacy framework for quantum systems that addresses limitations of quantum differential privacy by allowing specification of private information, measurements, and domain knowledge. They showed QPP can be formulated using the Datta-Leditzky information spectrum divergence, proved its convexity and composability, derived parameters for the depolarization mechanism, and applied it to privacy auditing with quantum algorithms.
We propose a versatile privacy framework for quantum systems, termed quantum pufferfish privacy (QPP). Inspired by classical pufferfish privacy, our formulation generalizes and addresses limitations of quantum differential privacy by offering flexibility in specifying private information, feasible measurements, and domain knowledge. We show that QPP can be equivalently formulated in terms of the Datta-Leditzky information spectrum divergence, thus providing the first operational interpretation thereof. We reformulate this divergence as a semi-definite program and derive several properties of it, which are then used to prove convexity, composability, and post-processing of QPP mechanisms. Parameters that guarantee QPP of the depolarization mechanism are also derived. We analyze the privacy-utility tradeoff of general QPP mechanisms and, again, study the depolarization mechanism as an explicit instance. The QPP framework is then applied to privacy auditing for identifying privacy violations via a hypothesis testing pipeline that leverages quantum algorithms. Connections to quantum fairness and other quantum divergences are also explored and several variants of QPP are examined.