Title:
Evaluating the security of anonymized big graph/structural data
Evaluating the security of anonymized big graph/structural data
Author(s)
Ji, Shouling
Advisor(s)
Bloch, Matthieu R.
Editor(s)
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Abstract
We studied the security of anonymized big graph data. Our main contributions include: new De-Anonymization (DA) attacks, comprehensive anonymity, utility, and de-anonymizability quantifications, and a secure graph data publishing/sharing system SecGraph. New DA Attacks. We present two novel graph DA frameworks: cold start single-phase Optimization-based DA (ODA) and De-anonymizing Social-Attribute Graphs (De-SAG). Unlike existing seed-based DA attacks, ODA does not priori knowledge. In addition, ODA’s DA results can facilitate existing DA attacks by providing more seed information. De-SAG is the first attack that takes into account both graph structure and attribute information. Through extensive evaluations leveraging real world graph data, we validated the performance of both ODA and De-SAG. Graph Anonymity, Utility, and De-anonymizability Quantifications. We developed new techniques that enable comprehensive graph data anonymity, utility, and de-anonymizability evaluation. First, we proposed the first seed-free graph de-anonymizability quantification framework under a general data model which provides the theoretical foundation for seed-free SDA attacks. Second, we conducted the first seed-based quantification on the perfect and partial de-anonymizability of graph data. Our quantification closes the gap between seed-based DA practice and theory. Third, we conducted the first attribute-based anonymity analysis for Social-Attribute Graph (SAG) data. Our attribute-based anonymity analysis together with existing structure-based de-anonymizability quantifications provide data owners and researchers a more complete understanding of the privacy of graph data. Fourth, we conducted the first graph Anonymity-Utility-De-anonymity (AUD) correlation quantification and provided close-forms to explicitly demonstrate such correlation. Finally, based on our quantifications, we conducted large-scale evaluations leveraging 100+ real world graph datasets generated by various computer systems and services. Using the evaluations, we demonstrated the datasets’ anonymity, utility, and de-anonymizability, as well as the significance and validity of our quantifications. SecGraph. We designed, implemented, and evaluated the first uniform and open-source Secure Graph data publishing/sharing (SecGraph) system. SecGraph enables data owners and researchers to conduct accurate comparative studies of anonymization/DA techniques, and to comprehensively understand the resistance/vulnerability of existing or newly developed anonymization techniques, the effectiveness of existing or newly developed DA attacks, and graph and application utilities of anonymized data.
Sponsor
Date Issued
2016-01-08
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Resource Type
Text
Resource Subtype
Dissertation