Generating synthetic graphs for large sensitive and correlated social networks

Xin Ju, Xiaofeng Zhang, Kwok Wai CHEUNG

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

With the fast development of social networks, there exists a huge amount of users information as well as their social ties. Such information generally contains sensitive and correlated users' personal data. How to accurately analyze this large and correlated social graph data while protecting users' privacy has become a challenging research issue. In the literature, various research efforts have been made on this topic. Unfortunately, correlation based privacy protection techniques for social graph data have seldom been investigated. To the best of our knowledge, this paper is the first attempt to resolve this research issue. Particularly, this paper first protects users' raw data via local differential privacy technique and then a correlation based privacy protection approach is designed. Last, a K-means algorithm is applied on the perturbed local data and the clustering results are used to generate the synthetic graphs for further data analytics. Experiments are evaluated on two real-world data sets, i.e. Facebook dataset and Enron dataset, and the promising experimental results demonstrate that the proposed approach is superior to the state-of-the-art LDPGen and the baseline method, e.g. the DGG, with respect to the accuracy and utility evaluation criteria.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering Workshops, ICDEW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages286-293
Number of pages8
ISBN (Electronic)9781728108902
DOIs
Publication statusPublished - Apr 2019
Event35th IEEE International Conference on Data Engineering Workshops, ICDEW 2019 - Macau, China
Duration: 8 Apr 201912 Apr 2019

Publication series

NameProceedings - 2019 IEEE 35th International Conference on Data Engineering Workshops, ICDEW 2019

Conference

Conference35th IEEE International Conference on Data Engineering Workshops, ICDEW 2019
Country/TerritoryChina
CityMacau
Period8/04/1912/04/19

Scopus Subject Areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Artificial Intelligence

User-Defined Keywords

  • Local Differential Privacy
  • Social Graph
  • Social Network Analysis

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