Abstract
Given the growing rates and richness of data being collected nowadays, it is non-trivial for data owners to determine a single best publishing granularity that presents the most value of the data while preserving its privacy. There have been extensive studies on privacy preserving algorithms in the data mining community, but relatively few have been done to provide a supervised control over the anonymization process. We present the design and evaluation of a visual interface that assists users to employ commonly used data anonymization techniques for making privacy preserving visualizations of the data. We focus on event sequence data due to its vulnerability to privacy concerns. Our visual 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 requests. Case studies using multiple datasets under different scenarios demonstrate the effectiveness of our design. These studies show that using visualization as an interface can help identify potential privacy issues, reveal underlying anonymization processes, and allow users to balance between data utility and privacy.
| Original language | English (US) |
|---|---|
| Title of host publication | SA 2016 - SIGGRAPH ASIA 2016 Symposium on Visualization |
| Publisher | Association for Computing Machinery, Inc |
| ISBN (Electronic) | 9781450345477 |
| DOIs | |
| State | Published - Nov 28 2016 |
| Event | 2016 SIGGRAPH ASIA Symposium on Visualization, SA 2016 - Macau, China Duration: Dec 5 2016 → Dec 8 2016 |
Other
| Other | 2016 SIGGRAPH ASIA Symposium on Visualization, SA 2016 |
|---|---|
| Country | China |
| City | Macau |
| Period | 12/5/16 → 12/8/16 |
Fingerprint
Keywords
- Event sequence data analysis
- Privacy preserving visualization
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Software
- Computer Graphics and Computer-Aided Design
Cite this
Privacy preserving event sequence data visualization using a sankey diagram-like representation. / Chou, Jia Kai; Wang, Yang; Ma, Kwan-Liu.
SA 2016 - SIGGRAPH ASIA 2016 Symposium on Visualization. Association for Computing Machinery, Inc, 2016. a1.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Privacy preserving event sequence data visualization using a sankey diagram-like representation
AU - Chou, Jia Kai
AU - Wang, Yang
AU - Ma, Kwan-Liu
PY - 2016/11/28
Y1 - 2016/11/28
N2 - Given the growing rates and richness of data being collected nowadays, it is non-trivial for data owners to determine a single best publishing granularity that presents the most value of the data while preserving its privacy. There have been extensive studies on privacy preserving algorithms in the data mining community, but relatively few have been done to provide a supervised control over the anonymization process. We present the design and evaluation of a visual interface that assists users to employ commonly used data anonymization techniques for making privacy preserving visualizations of the data. We focus on event sequence data due to its vulnerability to privacy concerns. Our visual 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 requests. Case studies using multiple datasets under different scenarios demonstrate the effectiveness of our design. These studies show that using visualization as an interface can help identify potential privacy issues, reveal underlying anonymization processes, and allow users to balance between data utility and privacy.
AB - Given the growing rates and richness of data being collected nowadays, it is non-trivial for data owners to determine a single best publishing granularity that presents the most value of the data while preserving its privacy. There have been extensive studies on privacy preserving algorithms in the data mining community, but relatively few have been done to provide a supervised control over the anonymization process. We present the design and evaluation of a visual interface that assists users to employ commonly used data anonymization techniques for making privacy preserving visualizations of the data. We focus on event sequence data due to its vulnerability to privacy concerns. Our visual 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 requests. Case studies using multiple datasets under different scenarios demonstrate the effectiveness of our design. These studies show that using visualization as an interface can help identify potential privacy issues, reveal underlying anonymization processes, and allow users to balance between data utility and privacy.
KW - Event sequence data analysis
KW - Privacy preserving visualization
UR - http://www.scopus.com/inward/record.url?scp=85006944177&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006944177&partnerID=8YFLogxK
U2 - 10.1145/3002151.3002153
DO - 10.1145/3002151.3002153
M3 - Conference contribution
BT - SA 2016 - SIGGRAPH ASIA 2016 Symposium on Visualization
PB - Association for Computing Machinery, Inc
ER -