Edge Deletion based Subgraph Hiding

Abstract

Extracting subgraphs from graph data is a challenging and important subgraph mining task since they reveal valuable insights in many domains. However, in the data sharing scenario, some of the subgraphs might be considered as sensitive by the data owner and require hiding before publishing the data. Therefore, subgraph hiding is applied to the data so that when subgraph mining algorithms, such as frequent subgraph mining, subgraph counting, or subgraph matching, are executed on this published data, sensitive subgraphs will not appear. While protecting the privacy of the sensitive subgraphs through hiding, the side effects should be kept at a minimum. In this paper, we address the problem of hiding sensitive subgraphs on graph data and propose an Edge deletion-based heuristic (EDH) algorithm. We evaluate our algorithm using three graph datasets and compare the results with the previous vertex masking heuristic algorithms in terms of execution time and side effects in the context of frequent subgraph hiding. The experimental results demonstrate that the EDH is competitive concerning execution time and outperforms the existing masking heuristic algorithms in terms of side effects by reducing information loss of non-sensitive patterns significantly and not creating fake patterns.