World Wide Web

, Volume 18, Issue 5, pp 1481–1517 | Cite as

Privacy preserving graph publication in a distributed environment



Nowadays, more and more people join different social networks to share or comment on their daily activities. Along with the popular usage of social networks, people’s privacy becomes a big concern. Therefore, recently, many works studied how to publish privacy preserving social networks for ”safely” data mining or analysis. These works all assume that there exists a single publisher who holds the complete graph. While, in real life, people join different social networks for different purposes. As a result, there are a group of publishers and each of them holds only a subgraph. Since no one has the complete graph, it is a challenging problem to generate the published graph in the distributed environment without releasing any publisher’s local content. In this paper, we propose an SMC (Secure Multi-Party Computation) based protocol to publish a privacy preserving graph in a distributed environment. We prove that our scheme can publish a privacy preserving graph without leaking the local content information and meanwhile achieve the maximum graph utility. We show the effectiveness of the protocol on real social networks under different distributed storage cases.


Soical network Privacy 


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Mingxuan Yuan
    • 1
    • 2
  • Lei Chen
    • 2
  • Philip S. Yu
    • 3
  • Hong Mei
    • 4
  1. 1.Huawei Noah Ark LabHong KongChina
  2. 2.The Hong Kong University of Science and TechnologyHong KongChina
  3. 3.University of Illinois at ChicagoChicagoUSA
  4. 4.Peking UniversityBei JingChina

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