A Utility-Aware Visual Approach for Anonymizing Multi-Attribute Tabular Data

Xumeng Wang, Jia Kai Chou, Wei Chen, Huihua Guan, Wenlong Chen, Tianyi Lao, Kwan-Liu Ma

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Sharing data for public usage requires sanitization to prevent sensitive information from leaking. Previous studies have presented methods for creating privacy preserving visualizations. However, few of them provide sufficient feedback to users on how much utility is reduced (or preserved) during such a process. To address this, we design a visual interface along with a data manipulation pipeline that allows users to gauge utility loss while interactively and iteratively handling privacy issues in their data. Widely known and discussed types of privacy models, i.e., syntactic anonymity and differential privacy, are integrated and compared under different use case scenarios. Case study results on a variety of examples demonstrate the effectiveness of our approach.

Original languageEnglish (US)
Article number8019828
Pages (from-to)351-360
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume24
Issue number1
DOIs
StatePublished - Jan 1 2018

Keywords

  • differential privacy
  • Privacy preserving visualization
  • syntactic anonymity
  • utility aware anonymization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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