论文标题
静态平均共识问题中的确定性隐私保护
Deterministic Privacy Preservation in Static Average Consensus Problem
论文作者
论文摘要
在本文中，我们考虑了静态平均共识问题中隐私保存的问题。通常，通过为流行的一阶基于拉普拉斯的算法提出隐私保护扩展来解决此问题。但是，这些机制与计算开销有关，可能需要在代理之间进行协调以选择其参数并改变算法的瞬态响应。在本文中，我们表明，在动态平均共识问题的背景下，文献中提出的另一种迭代算法具有固有的隐私保护，可以用作保存算法的隐私性，该算法具有与众所周知的Laplacian Comsensus算法相同的性能行为，但没有现有的私有性保留方法。
In this paper we consider the problem of privacy preservation in the static average consensus problem. This problem normally is solved by proposing privacy preservation augmentations for the popular first order Laplacian-based algorithm. These mechanisms however come with computational overhead, may need coordination among the agents to choose their parameters and also alter the transient response of the algorithm. In this paper we show that an alternative iterative algorithm that is proposed in the literature in the context of dynamic average consensus problem has intrinsic privacy preservation and can be used as a privacy preserving algorithm that yields the same performance behavior as the well-known Laplacian consensus algorithm but without the overheads that come with the existing privacy preservation methods.
