Authors: Xumeng Wang, Jia-Kai Chou, Wei Chen, Huihua Guan, Wenlong Chen, tianyi lao, Kwan-Liu Ma
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 allow 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.