发表机构
KAIST; UC San Diego（韩国科学技术院; 加州大学圣迭戈分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出算子范数方法，统一量子随机预言机与随机排列模型中的非均匀安全性归约，实现Yao盒子紧致界及决策游戏最优加盐定理，并改进伪随机生成器界。
AI 中文摘要
非均匀安全性允许敌手在尝试新的挑战之前接收关于预言机的有界建议。这捕捉了最现实的攻击场景，并已在先前的工作中被广泛研究。在本工作中，我们引入了一种算子范数方法，用于量子随机预言机和随机排列模型中的非均匀安全性。这种新方法为搜索成功概率和区分优势提供了统一的归约。此前，即使在决策游戏中，归约也仅适用于成功概率，从而产生更差的界。我们的框架为Yao的盒子（无论是否加盐）提供了紧致界，并为伪随机生成器提供了改进的界。我们还证明了决策游戏的一个最优通用加盐定理。通过定义游戏的一个属性，该属性分离了离线阶段已有查询与后续在线查询的贡献，我们为特定的加盐构造获得了更强的界。这些包括加盐排列求逆（紧致到对数因子）和加盐随机函数求逆（紧致到对数因子以及经典函数求逆中已存在的差距）。
英文摘要
Non-uniform security allows an adversary to receive bounded advice about an oracle before attempting a fresh challenge. This captures the most realistic attacks and has already been studied extensively in prior work. In this work, we introduce an operator-norm approach for non-uniform security in the quantum random oracle and random permutation models. This new approach yields a unified reduction for both search success probability and distinguishing advantage. Previously, the reduction only worked with success probability even in the decision games, yielding a worse bound. Our framework enables tight bounds for Yao's box, both with and without salting, and improved bounds for pseudorandom generators. We also prove an optimal generic salting theorem for decision games. By defining a property of a game, which separates the contributions of the existing queries in the offline stage and subsequent online queries, we obtain stronger bounds for specific salted constructions. These include salted permutation inversion, tight up to logarithmic factors, and salted random-function inversion, tight up to logarithmic factors and the gap already present in classical function inversion.
