Graph Watermarks
This addresses data leakage issues for owners of sensitive graph datasets, such as social networks or network topologies, offering a novel security mechanism.
The paper tackles the problem of securely sharing sensitive graph datasets by introducing graph watermarks, which are small graphs embedded into the original graph to deter and detect leaks, with experiments on large real graphs showing they are unique and difficult to forge.
From network topologies to online social networks, many of today's most sensitive datasets are captured in large graphs. A significant challenge facing owners of these datasets is how to share sensitive graphs with collaborators and authorized users, e.g. network topologies with network equipment vendors or Facebook's social graphs with academic collaborators. Current tools can provide limited node or edge privacy, but require modifications to the graph that significantly reduce its utility. In this work, we propose a new alternative in the form of graph watermarks. Graph watermarks are small graphs tailor-made for a given graph dataset, a secure graph key, and a secure user key. To share a sensitive graph G with a collaborator C, the owner generates a watermark graph W using G, the graph key, and C's key as input, and embeds W into G to form G'. If G' is leaked by C,its owner can reliably determine if the watermark W generated for C does in fact reside inside G', thereby proving C is responsible for the leak. Graph watermarks serve both as a deterrent against data leakage and a method of recourse after a leak. We provide robust schemes for creating, embedding and extracting watermarks, and use analysis and experiments on large, real graphs to show that they are unique and difficult to forge. We study the robustness of graph watermarks against both single and powerful colluding attacker models, then propose and empirically evaluate mechanisms to dramatically improve resilience.