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GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms.

Xumeng Wang, Wei Chen, Jia-Kai Chou

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    Summary
    This summary is machine-generated.

    GraphProtector offers a visual interface for hybrid privacy protection in social network analysis. It helps users combine anonymization techniques while preserving data utility, addressing a key challenge in the field.

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    Area of Science:

    • Computer Science
    • Data Privacy
    • Network Analysis

    Background:

    • Social network analysis reveals relationships but risks privacy by exposing real-world identities.
    • Existing anonymization strategies often focus on single techniques and struggle with evaluating combined approaches and data utility.

    Purpose of the Study:

    • To introduce GraphProtector, a visual interface for guiding users through a privacy preservation pipeline in social network analysis.
    • To enable the simultaneous combination of multiple privacy protection schemes for a hybrid approach.

    Main Methods:

    • Development of GraphProtector, a visual interface for privacy preservation.
    • Implementation of a pipeline allowing users to combine multiple anonymization schemes.
    • Evaluation through case studies and interviews with expert users.

    Main Results:

    • GraphProtector facilitates the application of hybrid anonymization strategies.
    • The interface aids users in selecting and combining privacy protection techniques.
    • Case studies and expert feedback demonstrate the system's effectiveness.

    Conclusions:

    • GraphProtector addresses the challenge of selecting and combining anonymization techniques for social network data.
    • The visual interface supports hybrid privacy preservation while considering data utility.
    • Expert users found GraphProtector effective in various scenarios.