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.
Highlights
In recent years, social networking sites such as Weibo, WeChat, Facebook, LinkedIn, and Twitter have changed the way people communicate online
(3) Experimental results on different real data sets show that compared with the node triangle counting distribution publishing, the edge triangle counting distribution publishing method based on the triangle-count sort edgeremoval algorithm (TSER) algorithm proposed in this paper can better retain the structural characteristics of the original graph and improve the availability of the published data
Background knowledge attack means that the attacker has mastered some network structure or attribute information to identify the target of the attack
Summary
Social networking sites such as Weibo, WeChat, Facebook, LinkedIn, and Twitter have changed the way people communicate online. For the publishing method of node triangle counting distribution, a large number of edges need to be removed to meet the node threshold, which leads to serious loss of graph data information and low data availability. Erefore, this paper proposes an edge triangle counting distribution publishing method, which can retain more graph information and improve data availability when meeting the edge threshold. In order to further reduce the sensitivity of triangle counting publishing under differential privacy constraints, this paper proposes a new projection method. (3) Experimental results on different real data sets show that compared with the node triangle counting distribution publishing, the edge triangle counting distribution publishing method based on the TSER algorithm proposed in this paper can better retain the structural characteristics of the original graph and improve the availability of the published data.
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