Consider a sub-population of rebels that wish to initiate a revolution. In order to avoid initializing a failed revolution, rebels would first strive to estimate their relative "power", which is often correlated with their fraction in the population. However, and especially in non-democratic countries, rebels refrain from disclosing themselves. This poses a significant challenge for rebels: estimating their fraction in the population while minimizing the risk of being identified as rebels. This paper introduces a distributed computing framework aiming to study this question. Our main takeaway message is that the communication pattern has a crucial role in achieving such a task. Specifically, we show that relying on the inherent noise in the communication, "public communication", characterized by the fact that each message announced by an individual can be viewed by all its neighbors, allows rebels to estimate their fraction in the population while keeping a negligible risk of each rebel being identified as such. The suggested estimation protocol, inspired by historical events, is extremely simple and can be executed covertly even under extreme conditions of surveillance. Conversely, we show that under peer-to-peer communication, protocols of similar simplicity are either inefficient or non-covert.
翻译:考虑想要发动革命的反叛分子的亚群。 为避免爆发失败的革命,反叛分子将首先努力估计其相对的“力量 ”, 而这往往与人口中的分数相关。然而,特别是在非民主国家,反叛分子不披露自己。这给反叛分子带来了重大挑战:估计其在人口中的分数,同时尽量减少被确定为叛乱分子的风险。本文提出了一个分散的计算框架,旨在研究这一问题。我们的主要传递信息是,通信模式在完成这项任务中起着关键作用。具体地说,我们表明,依靠通信中固有的噪音,即“公共通信 ”, 其特征是,个人宣布的每条信息都可以被其所有邻居看到,使反叛分子能够估计其在人口中的分数,同时将每个被确定为反叛者的风险微乎其微。建议的估算协议受到历史事件的启发,非常简单,甚至可以在极端监视条件下秘密执行。相反,我们表明,在同行通信中,类似简单易懂的规程要么效率低下,要么是非隐蔽的。