Locally Differentially Private Heterogeneous Graph Aggregation with Utility Optimization

Graph data are widely collected and exploited by organizations, providing convenient services from policy formation and market decisions to medical care and social interactions. Yet, recent exposures of private data abuses have caused huge financial and reputational costs to both organizations and their users, enabling designing efficient privacy protection mechanisms a top priority. Local differential privacy (LDP) is an emerging privacy preservation standard and has been studied in various fields, including graph data aggregation. However, existing research studies of graph aggregation with LDP mainly provide single edge privacy for pure graph, leaving heterogeneous graph data aggregation with stronger privacy as an open challenge. In this paper, we take a step toward simultaneously collecting mixed attributed graph data while retaining intrinsic associations, with stronger local differential privacy protecting more than single edge. Specifically, we first propose a moderate granularity attributewise local differential privacy (ALDP) and formulate the problem of aggregating mixed attributed graph data as collecting two statistics under ALDP. Then we provide mechanisms to privately collect these statistics. For the categorical-attributed graph, we devise a utility-improved PrivAG mechanism, which randomizes and aggregates subsets of attribute and degree vectors. For heterogeneous graph, we present an adaptive binning scheme (ABS) to dynamically segment and simultaneously collect mixed attributed data, and extend the prior mechanism to a generalized PrivHG mechanism based on it. Finally, we practically optimize the utility of the mechanisms by reducing the computation costs and estimation errors. The effectiveness and efficiency of the mechanisms are validated through extensive experiments, and better performance is shown compared with the state-of-the-art mechanisms.