Publishing Triangle Counting Histogram in Social Networks Based on Differential Privacy

Abstract

The continuous expansion of the number and scale of social networking sites has led to an explosive growth of social network data. Mining and analyzing social network data can bring huge economic value and social benefits, but it will result in privacy leakage and other issues. The research focus of social network data publishing is to publish available data while ensuring privacy. Aiming at the problem of low data availability of social network node triangle counting publishing under differential privacy, this paper proposes a privacy protection method of edge triangle counting. First, an edge-removal projection algorithm TSER based on edge triangle count sorting is proposed to obtain the upper bound of sensitivity. Then, two edge triangle count histogram publishing methods satisfying edge difference privacy are given based on the TSER algorithm. Finally, experimental results show that compared with the existing algorithms, the TSER algorithm can retain more triangles in the original graph, reduce the error between the published data and the original data, and improve the published data availability.

A Node Differential Privacy-Based Method to Preserve Directed Graphs in Wireless Mobile Networks

“…To accurately estimate subgraph counts, [30] proposed a novel multi-phase framework under DDP (decentralized differential privacy), which was able to control the minimum local noise scale to preserve the sub-graph counts. Furthermore, some statistical data in graph data, such as triangle counts, centrality and shortest paths were preserved by differential privacy before they were released [31,32]. Apart from preserving the statistical data, differential privacy is also applied to generate a synthetic graph.…”