静态平均一致性问题中的确定性隐私保护
Deterministic Privacy Preservation in Static Average Consensus Problem
摘要：
本文研究了静态平均一致性问题中的隐私保护问题。这个问题通常是通过提出隐私保护增强流行的一阶拉普拉斯算法来解决。然而，这些机制带来了计算开销，可能需要代理之间的协调来选择它们的参数，并且还改变算法的瞬态响应。在本文中，我们表明，一种替代的迭代算法，在文献中提出的动态平均共识问题的背景下，具有内在的隐私保护，并可以作为一个隐私保护算法，产生相同的性能行为作为著名的拉普拉斯共识算法，但没有开销，来与现有的隐私保护方法。
英文摘要：
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.
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