Injecting Uncertainty in Graphs for Identity Obfuscation
Data collected nowadays by social-networking applications create fascinating opportunities for building novel services, as well as expanding the authors' understanding about social structures and their dynamics. Unfortunately, publishing social-network graphs is considered an ill-advised practice due to privacy concerns. To alleviate this problem, several anonymization methods have been proposed, aiming at reducing the risk of a privacy breach on the published data, while still allowing analyzing them and drawing relevant conclusions. In this paper, they introduce a new anonymization approach that is based on injecting uncertainty in social graphs and publishing the resulting uncertain graphs.