Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems
Theshani Nuradha, Ziv Goldfeld, Mark M. Wilde

TL;DR
This paper introduces quantum pufferfish privacy (QPP), a flexible framework for protecting private information in quantum systems, generalizing quantum differential privacy and enabling privacy auditing with quantum algorithms.
Contribution
It formulates QPP using the Datta-Leditzky divergence, provides its operational interpretation, and develops properties like convexity and composability, advancing quantum privacy theory.
Findings
QPP can be expressed via the Datta-Leditzky divergence.
The divergence admits a semi-definite program formulation.
QPP mechanisms exhibit convexity, composability, and post-processing properties.
Abstract
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…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Mechanics and Applications · Quantum Computing Algorithms and Architecture
