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Privacy Preserving Graph Publication in a Distributed Environment

  • Mingxuan Yuan
  • Lei Chen
  • Philip S. Yu
  • Hong Mei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7808)

Abstract

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 a distributed environment without releasing any publisher’s local content. In this paper, we propose a SMC (Secure Multi-Party Computation) based protocol to publish a privacy preserving graph in a distributed environment. 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 a real social network under different distributed storage cases.

Keywords

Security Requirement Super Node Duplicate Weight Data Agent Connection Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mingxuan Yuan
    • 1
    • 2
  • Lei Chen
    • 2
  • Philip S. Yu
    • 3
  • Hong Mei
    • 4
  1. 1.Huawei Noah Ark LabHong Kong
  2. 2.The Hong Kong University of Science & TechnologyHong Kong
  3. 3.University of Illinois at ChicagoUSA
  4. 4.Peking UniversityChina

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