Privacy-Preserving Network Aggregation

Troy Raeder, Marina Blanton, Nitesh V. Chawla, Keith Frikken
Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2010. 198-207.
Publication Date: 
June, 2010

Consider the scenario where information about a large network is distributed across several different parties or commercial entities. Intuitively, we would expect that the aggregate network formed by combining the individual private networks would be a more faithful representation of the network phenomenon as a whole. However, privacy preservation of the individual networks becomes a mandate. Thus, it would be useful, given several portions of an underlying network p 1 ...p n , to securely compute the aggregate of all the networks p i in a manner such that no party learns information about any other party’s network. In this work, we propose a novel privacy preservation protocol for the non-trivial case of weighted networks. The protocol is secure against malicious adversaries.