Publishing Graph Node Strength Histogram with Edge Differential Privacy

  • Qing Qian
  • Zhixu Li
  • Pengpeng Zhao
  • Wei Chen
  • Hongzhi Yin
  • Lei ZhaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)


Protecting the private graph data while releasing accurate estimate of the data is one of the most challenging problems in data privacy. Node strength combines the topological information with the weight distribution of the weighted graph in a natural way. Since an edge in graph data oftentimes represents relationship between two nodes, edge-differential privacy (edge-DP) can protect relationship between two entities from being disclosed. In this paper, we investigate the problem of publishing the node strength histogram of a private graph under edge-DP. We propose two clustering approaches based on sequence-aware and local density to aggregate histogram. Our experimental study demonstrates that our approaches can greatly reduce the error of approximating the true node strength histogram.


Differential privacy Node strength Histogram publishing 



This work was supported by the National Natural Science Foundation of China under Grant Nos. 61572335, 61572336, 61472263, 61402312 and 61402313, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20151223, and Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Qing Qian
    • 1
  • Zhixu Li
    • 1
  • Pengpeng Zhao
    • 1
  • Wei Chen
    • 1
  • Hongzhi Yin
    • 2
  • Lei Zhao
    • 1
    Email author
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.The School of Information Technology and Electrical Engineering BrisbaneThe University of QueenslandSt LuciaAustralia

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