Discretionary social network data revelation with a user-centric utility guarantee

The proliferation of online social networks has created intense interest in studying their nature and revealing information of interest to the end user. At the same time, such revelation raises privacy concerns. Existing research addresses this problem following an approach popular in the database community: a model of data privacy is defined, and the data is rendered in a form that satisfies the constraints of that model while aiming to maximize some utility measure. Still, these is no consensus on a clear and quantifiable utility measure over graph data. In this paper, we take a different approach: we define a utility guarantee, in terms of certain graph properties being preserved, that should be respected when releasing data, while otherwise distorting the graph to an extend desired for the sake of confidentiality. We propose a form of data release which builds on current practice in social network platforms: A user may want to see a subgraph of the network graph, in which that user as well as connections and affiliates participate. Such a snapshot should not allow malicious users to gain private information, yet provide useful information for benevolent users. We propose a mechanism to prepare data for user view under this setting. In an experimental study with real data, we demonstrate that our method preserves several properties of interest more successfully than methods that randomly distort the graph to an equal extent, while withstanding structural attacks proposed in the literature.