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 Weiqing Li

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Average citations per article3.75
Citation Count15
Publication count4
Publication years2014-2016
Available for download3
Average downloads per article344.33
Downloads (cumulative)1,033
Downloads (12 Months)388
Downloads (6 Weeks)21
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1
December 2016 IEEE/ACM Transactions on Networking (TON): Volume 24 Issue 6, December 2016
Publisher: IEEE Press
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 3,   Downloads (12 Months): 54,   Downloads (Overall): 54

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In this paper, we study the quantification, practice, and implications of structural data de-anonymization, including social data, mobility traces, and so on. First, we answer several open questions in structural data de-anonymization by quantifying perfect and $1-\epsilon $ -perfect structural data de-anonymization, where $\epsilon $ ...

2 published by ACM
April 2016 ACM Transactions on Information and System Security (TISSEC): Volume 18 Issue 4, May 2016
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 11,   Downloads (12 Months): 242,   Downloads (Overall): 618

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When people utilize social applications and services, their privacy suffers a potential serious threat. In this article, we present a novel, robust, and effective de-anonymization attack to mobility trace data and social data. First, we design a Unified Similarity (US) measurement, which takes account of local and global structural characteristics ...
Keywords: Graph de-anonymization, mobility traces, social networks

3
August 2015 SEC'15: Proceedings of the 24th USENIX Conference on Security Symposium
Publisher: USENIX Association
Bibliometrics:
Citation Count: 5

In this paper, we analyze and systematize the state-of-the-art graph data privacy and utility techniques. 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, ...

4 published by ACM
November 2014 CCS '14: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security
Publisher: ACM
Bibliometrics:
Citation Count: 8
Downloads (6 Weeks): 7,   Downloads (12 Months): 92,   Downloads (Overall): 361

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In this paper, we study the quantification, practice, and implications of structural data (e.g., social data, mobility traces) De-Anonymization (DA). First, we address several open problems in structural data DA by quantifying perfect and (1-ε)-perfect structural data DA}, where ε is the error tolerated by a DA scheme. To the ...
Keywords: de-anonymization, structural data, quantification, evaluation, mobility traces, social networks



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