Year: 2024
Type: article
Abstract: We propose a versatile privacy framework for quantum systems, termedquantum 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.
Authors: Theshani Nuradha, Ziv Goldfeld, Mark M. Wilde
Institution: Cornell University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: NSF-BSF: Collaborative Research: CIF: Small: Neural Estimation of Statistical Divergences: Theoretical Foundations and Applications to Communication Systems, CIF: Small: Resource Theories of Quantum Channels, CAREER: Smooth statistical distances for a scalable learning theory, CIF: Small: Resource Theories of Quantum Channels, New Challenges in Statistical Inference with Regularized Optimal Transport
OpenAlex ID: W4398775113