Publishing Graph Degree Distribution with Node Differential Privacy

Wei-Yen Day, Ninghui Li, Min Lyu
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引用次数: 144

Abstract

Graph data publishing under node-differential privacy (node-DP) is challenging due to the huge sensitivity of queries. However, since a node in graph data oftentimes represents a person, node-DP is necessary to achieve personal data protection. In this paper, we investigate the problem of publishing the degree distribution of a graph under node-DP by exploring the projection approach to reduce the sensitivity. We propose two approaches based on aggregation and cumulative histogram to publish the degree distribution. The experiments demonstrate that our approaches greatly reduce the error of approximating the true degree distribution and have significant improvement over existing works. We also present the introspective analysis for understanding the factors of publishing the degree distribution with node-DP.
具有节点差分隐私的发布图度分布
由于查询的高度敏感性,在节点差分隐私(node-DP)下的图数据发布具有挑战性。然而,由于图数据中的节点通常代表一个人,因此需要node- dp来实现个人数据保护。本文通过探索投影法来降低灵敏度,研究了节点dp下图的度分布发布问题。我们提出了两种基于聚合和累积直方图的方法来发布度分布。实验表明,我们的方法大大减小了逼近真实度分布的误差,与现有的方法相比有了显著的改进。本文还提出了内省分析,以了解节点- dp发布度分布的影响因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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