Analysis of centrality measures under differential privacy models

Jesse Laeuchli, Yunior Ramírez-Cruz and Rolando Trujillo-Rasua

Applied Mathematics and Computation, 2022, vol. 412, issue C

Abstract: This article provides the first analysis of the differentially private computation of three centrality measures, namely eigenvector, Laplacian and closeness centralities, on arbitrary weighted graphs, using the smooth sensitivity approach. We do so by finding lower bounds on the amounts of noise that a randomised algorithm needs to add in order to make the output of each measure differentially private. Our results indicate that these computations are either infeasible, in the sense that there are large families of graphs for which smooth sensitivity is unbounded; or impractical, in the sense that even for the cases where smooth sensitivity is bounded, the required amounts of noise result in unacceptably large utility losses.

Keywords: Privacy; Graph; Centrality; Differential privacy; Social network (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:412:y:2022:i:c:s0096300321006305

DOI: 10.1016/j.amc.2021.126546

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