Accurate Estimation of the Degree Distribution of Private Networks

—We describe an efficient algorithm for releasing a provably private estimate of the degree distribution of a network. The algorithm satisfies a rigorous property of differential privacy, and is also extremely efficient, running on networks of 100 million nodes in a few seconds. Theoretical analysis shows that the error scales linearly with the number of unique degrees, whereas the error of conventional techniques scales linearly with the number of nodes. We complement the theoretical analysis with a thorough empirical analysis on real and synthetic graphs, showing that the algorithm’s variance and bias is low, that the error diminishes as the size of the input graph increases, and that common analyses like fitting a power-law can be carried out very accurately. Keywords-privacy; social networks; privacy-preserving data mining; differential privacy.

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Michael Hay, Chao Li, Gerome Miklau, David Jensen
• Keywords: Data Mining, Efficient Algorithm, Error Scales, ICDM 2009, Theoretical Analysis