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Shortest Paths and Distances with Differential Privacy
[article]

2016
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arXiv
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pre-print

We introduce a model for differentially private analysis of weighted graphs in which the graph topology (V,E) is assumed to be public and the private information consists only of the edge weights w:E→R^+. This can express hiding congestion patterns in a known system of roads. Differential privacy requires that the output of an algorithm provides little advantage, measured by privacy parameters ϵ and δ, for distinguishing between neighboring inputs, which are thought of as inputs that differ on

arXiv:1511.04631v2
fatcat:g6boiv7rgzcehp4k37i4nzx3iy