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SecGraph: A Uniform and Open-source Evaluation System for Graph Data Anonymization and De-anonymization

Shouling Ji, Weiqing Li, Prateek Mittal, Xin Hu, Raheem A. Beyah
2015 USENIX Security Symposium  
Specifically, we propose and develop SecGraph (available at [1]), a uniform and open-source Secure Graph data sharing/publishing system.  ...  In SecGraph, we systematically study, implement, and evaluate 11 graph data anonymization algorithms, 19 data utility metrics, and 15 modern Structure-based De-Anonymization (SDA) attacks.  ...  Acknowledgments The authors are very grateful to the anonymous reviewers for their time and valuable comments.  ... 
dblp:conf/uss/JiLMHB15 fatcat:hi6whddjsndtpetkrleat6q4xa

ShareSafe: An Improved Version of SecGraph

2019 KSII Transactions on Internet and Information Systems  
In this paper, we redesign, implement, and evaluate ShareSafe (Based on SecGraph), an open-source secure graph data sharing/publishing platform.  ...  To the best of our knowledge, ShareSafe is the first platform that enables users to perform data perturbation, utility evaluation, De-A evaluation, and Privacy Quantification.  ...  Acknowledgements The authors are very grateful to the anonymous reviewers for their time and valuable comments.  ... 
doi:10.3837/tiis.2019.11.025 fatcat:5cyczm6jx5dv7hrf5pesirvlxe

Using Metrics Suites to Improve the Measurement of Privacy in Graphs

Yuchen Zhao, Isabel Wagner
2020 IEEE Transactions on Dependable and Secure Computing  
In this paper, we study 26 privacy metrics for graph anonymization and de-anonymization and evaluate their strength in terms of three criteria: monotonicity indicates whether the metric indicates lower  ...  This important result enables more monotonic, and thus more accurate, evaluations of new graph anonymization and de-anonymization algorithms.  ...  The authors thank the anonymous reviewers for their insightful comments.  ... 
doi:10.1109/tdsc.2020.2980271 fatcat:nydqxcet7jfh3fjazqc7ets7a4

On the relative de-anonymizability of graph data: Quantification and evaluation

Shouling Ji, Weiqing Li, Shukun Yang, Prateek Mittal, Raheem Beyah
2016 IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications  
Specifically, we quantify both the seed-based and the seed-free Relative De-anonymizability (RD) of graph data for both perfect DA (successfully de-anonymizing all the target users) and partial DA (where  ...  In this paper, we propose a structural importanceaware approach to quantify the vulnerability/de-anonymizability of graph data to structure-based De-Anonymization (DA) attacks [1][2][3][4].  ...  ACKNOWLEDGMENT This work was supported in part by NSF awards number CNS-1409415 and CNS-1423139.  ... 
doi:10.1109/infocom.2016.7524585 dblp:conf/infocom/JiLYMB16 fatcat:qkxr5bw4x5f3fkuvddczk4bb6u