An Empirical Study on Perceptually Masking Privacy in Graph Visualizations

Jia Kai Chou, Chris Bryan, Jing Li, Kwan-Liu Ma

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

1 Scopus citations

Abstract

Researchers such as sociologists create visualizations of multivariate node-link diagrams to present findings about the relationships in communities. Unfortunately, such visualizations can inadvertently expose the ostensibly private identities of the persons that make up the dataset. By purposely violating graph readability metrics for a small region of the graph, we conjecture that local, exposed privacy leaks may be perceptually masked from easy recognition. In particular, we consider three commonly known metrics∗edge crossing, node clustering, and node-edge overlapping∗as a strategy to hide leaks. We evaluate the effectiveness of violating these metrics by conducting a user study that measures subject performance at visually searching for and identifying a privacy leak. Results show that when more masking operations are applied, participants needed more time to locate the privacy leak, though exhaustive, brute force search can eventually find it. We suggest future directions on how perceptual masking can be a viable strategy, primarily where modifying the underlying network structure is unfeasible.

Original languageEnglish (US)
Title of host publication2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018
EditorsStoney Trent, Jorn Kohlhammer, Graig Sauer, Robert Gove, Daniel Best, Celeste Lyn Paul, Nicolas Prigent, Diane Staheli
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538681947
DOIs
StatePublished - May 7 2019
Event2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018 - Berlin, Germany
Duration: Oct 22 2018 → …

Publication series

Name2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018

Conference

Conference2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018
CountryGermany
CityBerlin
Period10/22/18 → …

Keywords

  • Human-centered computing
  • Visualization
  • Visualization design and evaluation methods

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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  • Cite this

    Chou, J. K., Bryan, C., Li, J., & Ma, K-L. (2019). An Empirical Study on Perceptually Masking Privacy in Graph Visualizations. In S. Trent, J. Kohlhammer, G. Sauer, R. Gove, D. Best, C. L. Paul, N. Prigent, & D. Staheli (Eds.), 2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018 [8709181] (2018 IEEE Symposium on Visualization for Cyber Security, VizSec 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VIZSEC.2018.8709181