Deterministic Privacy Preservation in Static Average Consensus Problem

In this letter, we consider the problem of privacy preservation in static average consensus problem. This normally is solved by proposing augmentations for popular first order Laplacian-based algorithm. These mechanisms however come with computational overhead, may need coordination among agents to choose their parameters and also alter transient response show that an alternative iterative algorithm proposed literature context dynamic has intrinsic can be used as a preserving yields same performance behavior well-known Laplacian but without overheads existing methods.