发表机构
Macau University of Science and Technology; Xi’an Jiaotong University; Tianjin University of Science and Technology（澳门科技大学; 西安交通大学; 天津科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对非确定性有限状态自动机建模的部分可观测离散事件系统，定义四种新型K步及无限步匿名性，利用并发组合技术给出其充要条件与复杂度分析，并计算K步强、弱匿名性的K值上界。
AI 中文摘要
匿名性是一种信息流属性，用于在特定时刻针对观测结果提供系统信息非唯一性层面的隐私保护。离散事件系统语境下的K步匿名性，指在当前时刻之前最多K个观测步骤内，状态估计值不会是单元素集合；而无限步匿名性则是不考虑K限制的K步匿名性。本文针对由非确定性有限状态自动机建模的部分可观测离散事件系统，深入研究K步及无限步匿名性。首先，定义了K步及无限步的两种强类型与两种弱类型匿名性，由于考虑了强、弱匿名投影，这些概念与现有K步及无限步匿名性存在本质差异。接着，开发了一种利用并发组合技术验证这四种匿名性的新方法，基于构建的并发组合，给出了这四种匿名性可验证的充要条件及其复杂度分析。最后，计算了K步强匿名性与弱匿名性的K值上界。
英文摘要
Anonymity is an information flow property that provides privacy protection in the sense of non-uniqueness of system information at certain moments with respect to observations. The notion of $K$-step anonymity in the context of discrete-event systems characterizes the scenario that the state estimates cannot be a singleton within at most $K$ observational steps prior to the current instant, while infinite-step anonymity is the same as $K$-step anonymity without considering the limit on $K$. In this paper, we lucubrate $K$- and infinite-step anonymity for partially-observed discrete-event systems modeled by non-deterministic finite-state automata. First, we define two strong types and two weak types of $K$- and infinite-step anonymity that are fundamentally different from the existing notions of $K$- and infinite-step anonymity due to the consideration of strong and weak anonymous projections. Then, we develop a new methodology by exploiting the concurrent-composition technique to verify these four types of anonymity. Based on the constructed concurrent compositions, verifiable necessary and sufficient conditions for the four types of anonymity are provided, along with their complexity analysis. Finally, the upper bounds on $K$ for $K$-step strong anonymity and weak anonymity are computed.
