In multiagent dynamical systems, privacy protection corresponds to avoid disclosing the initial states of the agents while accomplishing a distributed task. The system-theoretic framework described in this paper for this scope, denoted dynamical privacy, relies on introducing output maps which act as masks, rendering the internal states of an agent indiscernible by the other agents. Our output masks are local (i.e., decided independently by each agent), time-varying functions asymptotically converging to the true states. The resulting masked system is also time-varying, and has the original unmasked system as its limit system. It is shown that dynamical privacy is not compatible with the existence of equilibria. Nevertheless the masked system retains the same convergence properties of the original system: the equilibria of the original systems become attractors for the masked system but lose the stability property. Application of dynamical privacy to popular examples of multiagent dynamics, such as models of social opinions, average consensus and synchronization, is investigated in detail.

在多代理动态系统中，隐私保护旨在避免在完成分布式任务时公开代理的初始状态。本文针对该范围描述的系统理论框架称为动态隐私，它依赖于引入充当遮罩的输出图，从而使其他主体无法区分一个主体的内部状态。我们的输出掩码是局部的（即，由每个代理独立确定），时变函数渐近地收敛到真实状态。最终的屏蔽系统也随时间变化，并且以原始的非屏蔽系统为极限系统。结果表明，动态隐私与均衡的存在是不相容的。然而，被掩盖的系统保留了与原始系统相同的收敛特性：原始系统的平衡成为掩蔽系统的吸引子，但失去了稳定性。详细研究了动态隐私在多主体动力学流行示例中的应用，例如社会意见，平均共识和同步模型。