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.

翻译：我们提出了一种适用于量子系统的通用隐私框架，称为量子河豚隐私（QPP）。受经典河豚隐私的启发，我们的框架通过灵活指定私有信息、可行测量和领域知识，推广并解决了量子差分隐私的局限性。我们证明QPP可以等价地用Datta-Leditzky信息谱散度表述，从而首次为其提供了操作化解释。我们将该散度重构为半定规划问题，并推导出其若干性质，进而用于证明QPP机制的凸性、可组合性和后处理特性。同时推导了保证去极化机制满足QPP的参数条件。我们分析了通用QPP机制的隐私-效用权衡关系，并以去极化机制为具体实例进行深入研究。随后将QPP框架应用于隐私审计，通过结合量子算法的假设检验流程来识别隐私违规行为。本文还探讨了QPP与量子公平性及其他量子散度的关联，并考察了QPP的若干变体。