量子安全非交互式归约
Quantum Secure Non-Interactive Reductions
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出量子安全非交互式归约（QSNIR），将经典SNIR扩展至双向量子态，通过半定规划精确计算隐私误差，并用于通用两方计算预处理关联。
中文摘要 AI 辅助
安全计算的高效模型通常依赖于离线预处理的关联，这些关联随后能够在最小假设下实现私密的在线计算。在这些模型中，安全非交互式归约（SNIR）的研究形式化了这样一种情形：一种经典关联可以被非交互地转换为另一种，同时保证基于信息论的模拟隐私。在本工作中，我们引入了量子安全非交互式归约（QSNIR），这是SNIR的自然扩展，其中源资源和目标资源均为双向量子态。具体而言，当目标资源为经典时，我们的框架使我们能够分析从纠缠以及经典资源中传播私有关联的问题。我们的主要技术结果是，在此背景下，QSNIR隐私可以通过一对半定规划（SDP）精确计算，该规划量化了一种操作上根植于基于模拟的作弊优势的误差。我们证明了QSNIR隐私由一个与最小误差态区分（MED）相关的决策问题下界约束，并利用这两种定义计算了通用两方计算（2PC）预处理关联的精确一次性隐私误差。
英文摘要
Efficient models for secure computation often rely on offline preprocessed correlations, which subsequently enable private online computation from minimal assumptions. Within these models, the study of secure non-interactive reductions (SNIR) formalizes when one classical correlation can be non-interactively transformed into another, while guaranteeing information-theoretic simulation-based privacy. In this work, we introduce quantum secure non-interactive reductions (QSNIR), a natural extension of SNIR, in which the source and target resource are each a bipartite quantum state. Specifically, when the target resource is classical, our framework enables us to analyze the problem of disseminating private correlations from entanglement, in addition to classical resources. Our primary technical result is that QSNIR privacy in this context can be computed exactly via a semidefinite program (SDP) pair that quantifies an error operationally rooted in a simulation-based cheating advantage. We prove that QSNIR privacy is lower-bounded by a decision problem related to minimum-error state discrimination (MED), and use both definitions to compute exact one-shot privacy errors for universal two-party computation (2PC) preprocessing correlations.
