Abstract
Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques—along with evaluating how applying the strategies will affect the utility of the anonymized results—remains a significant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.
| Original language | English (US) |
|---|---|
| Journal | IEEE Transactions on Visualization and Computer Graphics |
| DOIs | |
| State | Accepted/In press - Aug 19 2018 |
Fingerprint
Keywords
- Data privacy
- Data visualization
- Graph privacy
- k-anonymity
- Measurement
- Pipelines
- Privacy
- privacy preservation
- structural features
- Task analysis
- Visualization
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Computer Graphics and Computer-Aided Design
Cite this
GraphProtector : A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms. / Wang, Xumeng; Chen, Wei; Chou, Jia Kai; Bryan, Chris; Guan, Huihua; Chen, Wenlong; Pan, Rusheng; Ma, Kwan-Liu.
In: IEEE Transactions on Visualization and Computer Graphics, 19.08.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - GraphProtector
T2 - A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms
AU - Wang, Xumeng
AU - Chen, Wei
AU - Chou, Jia Kai
AU - Bryan, Chris
AU - Guan, Huihua
AU - Chen, Wenlong
AU - Pan, Rusheng
AU - Ma, Kwan-Liu
PY - 2018/8/19
Y1 - 2018/8/19
N2 - Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques—along with evaluating how applying the strategies will affect the utility of the anonymized results—remains a significant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.
AB - Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques—along with evaluating how applying the strategies will affect the utility of the anonymized results—remains a significant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.
KW - Data privacy
KW - Data visualization
KW - Graph privacy
KW - k-anonymity
KW - Measurement
KW - Pipelines
KW - Privacy
KW - privacy preservation
KW - structural features
KW - Task analysis
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85052627961&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052627961&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2018.2865021
DO - 10.1109/TVCG.2018.2865021
M3 - Article
AN - SCOPUS:85052627961
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
SN - 1077-2626
ER -