AI 中文总结
提出上下文链协议，利用共享上下文和内存瓶颈实现间歇连接设备的轻量级连续性认证，通过因果计数定理保证安全性，无需增加设备计算负担。
AI 中文摘要
认证能否让内存而非计算难度成为攻击者的瓶颈？上下文链是一种针对间歇连接设备的轻量级连续性协议，这些设备共享不断演变的物理或操作上下文。诚实的设备遵循一条已实现的历史路径，更新一个紧凑的累加器和基于哈希的固定就绪通道；中断会导致暂停或有界回滚，而非分支搜索。在时段冻结后，一个新的挑战会在短期限内选择一个通道。因此，错过上下文的局外人可能需要在得知哪个历史将被测试之前，为许多成熟的历史做好准备。在标准随机预言机模型中，一个因果计数定理对达到目标成功概率所需的截止期限可访问保留状态给出了下界，该下界针对任意的非线性预选择编码和自适应后选择查询，并考虑了顺序深度、候选查询以及在线获取的跨目标受保护信息。诚实设备的就绪内存保持固定，且与可能的历史数量无关。因此，上下文链将共享经验的不确定性转化为可调的准备工作要求，而无需将组合复杂性转移给轻量级设备。
英文摘要
Can authentication make memory, rather than computational hardness, the attacker's bottleneck? Contextual Chain is a lightweight continuity protocol for intermittently connected devices that share evolving physical or operational context. An honest device follows one realized history, updating a compact accumulator and fixed hash-based readiness lanes; outages cause pause or bounded rollback, not branch search. After the epoch is frozen, a fresh challenge selects one lane under a short deadline. An outsider that missed context may therefore need to prepare for many mature histories before learning which one will be tested. In the standard random-oracle model, a causal counting theorem lower-bounds the deadline-accessible retained state required for a target success probability against arbitrary nonlinear preselection encoding and adaptive post-selection queries, accounting for sequential depth, candidate queries, and cross-target protected information obtained online. Honest readiness memory remains fixed and independent of the number of plausible histories. Contextual Chain thus converts shared-experience uncertainty into a tunable preparation requirement without transferring combinatorial complexity to lightweight devices.
Comments31 pages, 6 figures
