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

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.

Privacy Preserving Visualization: A Study on Event Sequence Data

“…Developing a hybrid approach that combines both data mining and visualization techniques, however, serves as a promising future research topic. A similar concept is also mentioned in [WCC*18].…”

“…To address such issue, one possible direction is to help the user better assess the inducted uncertainty before sharing the privacy‐preserved visualization. In [WCC*18], Wang et al . provide a ‘delta chart’ that shows the exact value differences before and after a data set goes through the anonymization process.…”

“…Wang et al . [WCC*18] introduce a visualization tool that follows a data processing pipeline for addressing privacy issues in tabular data sets. In our previous study [CWM16], we utilize a set of well‐known data sanitization concepts and design a visual interface that facilitates the process of producing privacy preserving visualizations of temporal event sequence data.…”

A survey of visual analytics techniques for machine learning

“…As data have the risk of exposing sensitive information, several recent studies have focused on preserving data privacy during the data quality improvement process. For tabular data, Wang et al [41] developed a Privacy Exposure Risk Tree to display privacy exposure risks in the data and a Utility Preservation Degree Matrix to exhibit how the utility changes as privacy-preserving operations are applied. To preserve privacy in network datasets, Wang et al [40] presented a visual analytics system, GraphProtector.…”

Privacy in the Cloud: A Survey of Existing Solutions and Research Challenges

“…Operating slightly differently from most tools available, Wang et al [158] proposed a Utility-Aware Visual Approach for Anonymizing Multi-Attribute Tabular Data. This long name describes an approach that combines multiple methods and models for data anonymization (e.g., k-anonymity, ldiversity, and t-closeness).…”