Shortest Paths and Distances with Differential Privacy
Adam Sealfon · 2016
We introduce a model for differentially private analysis of weighted graphs in which the graph topology (υ,ε) is assumed to be public and the private information consists only of the edge weights ω : ε → 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 the contribution of one individual. In our model, two weight functions w,w' are considered to be neighboring if they have l1 distance at most one.