A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
SecGraph: A Uniform and Open-source Evaluation System for Graph Data Anonymization and De-anonymization
2015
USENIX Security Symposium
Preserved Fulltext
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
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2020
IEEE Transactions on Dependable and Secure Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2016
IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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