Privacy Preserving Visualization: A Study on Event Sequence Data
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Date
2019
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© 2019 The Eurographics Association and John Wiley & Sons Ltd.
Abstract
The inconceivable ability and common practice to collect personal data as well as the power of data‐driven approaches to businesses, services and security nowadays also introduce significant privacy issues. There have been extensive studies on addressing privacy preserving problems in the data mining community but relatively few have provided supervised control over the anonymization process. Preserving both the value and privacy of the data is largely a non‐trivial task. We present the design and evaluation of a visual interface that assists users in employing commonly used data anonymization techniques for making privacy preserving visualizations. Specifically, we focus on event sequence data due to its vulnerability to privacy concerns. Our interface is designed for data owners to examine potential privacy issues, obfuscate information as suggested by the algorithm and fine‐tune the results per their discretion. Multiple use case scenarios demonstrate the utility of our design. A user study similarly investigates the effectiveness of the privacy preserving strategies. Our results show that using a visual‐based interface is effective for identifying potential privacy issues, for revealing underlying anonymization processes, and for allowing users to balance between data utility and privacy.The inconceivable ability and common practice to collect personal data as well as the power of data‐driven approaches to businesses, services and security nowadays also introduce significant privacy issues. There have been extensive studies on addressing privacy preserving problems in the data mining community but relatively few have provided supervised control over the anonymization process. Preserving both the value and privacy of the data is largely a non‐trivial task. We present the design and evaluation of a visual interface that assists users in employing commonly used data anonymization techniques for making privacy preserving visualizations. Specifically, we focus on event sequence data due to its vulnerability to privacy concerns. Our interface is designed for data owners to examine potential privacy issues, obfuscate information as suggested by the algorithm and fine‐tune the results per their discretion.
Description
@article{10.1111:cgf.13535,
journal = {Computer Graphics Forum},
title = {{Privacy Preserving Visualization: A Study on Event Sequence Data}},
author = {Chou, Jia‐Kai and Wang, Yang and Ma, Kwan‐Liu},
year = {2019},
publisher = {© 2019 The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13535}
}