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Towards Differentially Private Aggregation of Heterogeneous Robots

机译:在异构机器人的差异私有聚集

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We are interested in securing the operation of robot swarms composed of heterogeneous agents that collaborate by exploiting aggregation mechanisms. Since any given robot type plays a role that may be critical in guaranteeing continuous and failure-free operation of the system, it is beneficial to conceal individual robot types and, thus, their roles. In our work, we assume that an adversary gains access to a description of the dynamic state of the swarm in its non-transient, nominal regime. We propose a method that quantifies how easy it is for the adversary to identify the type of any of the robots, based on this observation. We draw from the theory of differential privacy to propose a closed-form expression of the leakage of the system at steady-state. Our results show how this model enables an analysis of the leakage as system parameters vary; they also indicate design rules for increasing privacy in aggregation mechanisms.
机译:我们有兴趣保护由通过利用聚集机制协作的异构代理组成的机器人群的操作。 由于任何给定的机器人类型在保证系统的连续和无故障操作方面发挥作用可能是至关重要的,因此隐藏各个机器人类型是有益的,因此是它们的角色。 在我们的工作中,我们假设对手获得了在其非瞬态标称制度中获得了群体的动态状态的描述。 我们提出了一种方法,这些方法可以根据该观察定量对验证来识别任何机器人类型的容易程度。 我们从差异隐私理论中汲取了稳定状态的闭合形式表达系统的泄漏表达。 我们的结果表明,随着系统参数变化,该模型如何分析泄漏; 他们还表明了增加汇总机制隐私的设计规则。

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