Privacy preserving visualization for social network data with ontology information

Jia Kai Chou, Chris Bryan, Kwan-Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Analyzing social network data helps sociologists understand the behaviors of individuals and groups as well as the relationships between them. With additional ontology information, the semantics behind the network structure can be further explored. Unfortunately, creating network visualizations with these datasets for presentation can inadvertently expose the private and sensitive information of individuals that reside in the data. To deal with this problem, we generalize conventional data anonymization models (originally designed for relational data) and formally apply them in the context of privacy preserving ontological network visualization. We use these models to identify the privacy leaks that exist in a visualization, provide graph modification actions that remove and/or perceptually minimize the effect of the identified leaks, and discuss strategies for what types of privacy actions to choose depending on the context of the leaks. We implement an ontological visualization interface with associated privacy preserving operations, and demonstrate with two case studies using real-world datasets to show that our approach can identify and solve potential privacy issues while balancing overall graph readability and utility.

Original languageEnglish (US)
Title of host publication2017 IEEE Pacific Visualization Symposium, PacificVis 2017 - Proceedings
EditorsYingcai Wu, Daniel Weiskopf, Tim Dwyer
PublisherIEEE Computer Society
Pages11-20
Number of pages10
ISBN (Electronic)9781509057382
DOIs
StatePublished - Sep 11 2017
Event10th IEEE Pacific Visualization Symposium, PacificVis 2017 - Seoul, Korea, Republic of
Duration: Apr 18 2017Apr 21 2017

Other

Other10th IEEE Pacific Visualization Symposium, PacificVis 2017
CountryKorea, Republic of
CitySeoul
Period4/18/174/21/17

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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  • Cite this

    Chou, J. K., Bryan, C., & Ma, K-L. (2017). Privacy preserving visualization for social network data with ontology information. In Y. Wu, D. Weiskopf, & T. Dwyer (Eds.), 2017 IEEE Pacific Visualization Symposium, PacificVis 2017 - Proceedings (pp. 11-20). [8031573] IEEE Computer Society. https://doi.org/10.1109/PACIFICVIS.2017.8031573