Sharing uncertain graphs using syntactic private graph models

Many graphs in social and business applications are not deterministic, but are uncertain in nature. Related research requires open access to these uncertain graphs. While sharing these datasets often risks exposing sensitive user data to the public. However, current graph anonymization works only target on deterministic graphs and overlook the uncertain scenario. Our work seeks a solution to release uncertain graphs with high utility without compromising user privacy. We show that simply combining the representative extraction strategy and conventional graph anonymization method will result in the addition of noise that significantly disrupts uncertain graph structure. Instead, we introduce an uncertainty-Aware method, Chameleon, that provides identical privacy guarantees with much less noise. With the possible world semantics, it enables a fine-grained control over the injected noise. Finally, we apply our method to real uncertain graphs and show that it produces anonymized uncertain graphs that closely match the originals in graph structure statistics.