A Utility-aware Visual Approach for Anonymizing Multi-attribute Tabular Data

被引：41

作者：

Wang, Xumeng ^{[1
]}

Chou, Jia-Kai ^{[2
]}

Chen, Wei ^{[1
]}

Guan, Huihua ^{[1
]}

Chen, Wenlong ^{[1
]}

Lao, Tianyi ^{[1
]}

Ma, Kwan-Liu ^{[2
]}

机构：

[1] Zhejiang Univ, Hangzhou, Zhejiang, Peoples R China

[2] Univ Calif Davis, Davis, CA 95616 USA

来源：

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS | 2018年 / 24卷 / 01期

基金：

国家自然科学基金重大项目; 美国国家科学基金会; 中国国家自然科学基金;

关键词：

Privacy preserving visualization; utility aware anonymization; syntactic anonymity; differential privacy; PRIVACY;

D O I：

10.1109/TVCG.2017.2745139

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：351 / 360

页数：10

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