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 Dong Su

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Bibliometrics: publication history
Average citations per article9.50
Citation Count57
Publication count6
Publication years2012-2017
Available for download6
Average downloads per article440.50
Downloads (cumulative)2,643
Downloads (12 Months)855
Downloads (6 Weeks)120
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1 published by ACM
October 2017 ACM Transactions on Privacy and Security (TOPS): Volume 20 Issue 4, October 2017
Publisher: ACM
Citation Count: 0
Downloads (6 Weeks): 39,   Downloads (12 Months): 102,   Downloads (Overall): 102

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k -means clustering is a widely used clustering analysis technique in machine learning. In this article, we study the problem of differentially private k -means clustering. Several state-of-the-art methods follow the single-workload approach, which adapts an existing machine-learning algorithm by making each step private. However, most of them do not ...
Keywords: Differential privacy, k-means clustering, private data publishing

February 2017 Proceedings of the VLDB Endowment: Volume 10 Issue 6, February 2017
Publisher: VLDB Endowment
Citation Count: 0
Downloads (6 Weeks): 9,   Downloads (12 Months): 74,   Downloads (Overall): 74

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The Sparse Vector Technique (SVT) is a fundamental technique for satisfying differential privacy and has the unique quality that one can output some query answers without apparently paying any privacy cost. SVT has been used in both the interactive setting, where one tries to answer a sequence of queries that ...

3 published by ACM
March 2016 CODASPY '16: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy
Publisher: ACM
Citation Count: 1
Downloads (6 Weeks): 43,   Downloads (12 Months): 353,   Downloads (Overall): 612

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There are two broad approaches for differentially private data analysis. The interactive approach aims at developing customized differentially private algorithms for various data mining tasks. The non-interactive approach aims at developing differentially private algorithms that can output a synopsis of the input dataset, which can then be used to support ...
Keywords: differential privacy, private data publishing, k-means clustering

4 published by ACM
November 2013 CCS '13: Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Publisher: ACM
Citation Count: 8
Downloads (6 Weeks): 10,   Downloads (12 Months): 142,   Downloads (Overall): 870

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We introduce a novel privacy framework that we call Membership Privacy. The framework includes positive membership privacy, which prevents the adversary from significantly increasing its ability to conclude that an entity is in the input dataset, and negative membership privacy, which prevents leaking of non-membership. These notions are parameterized by ...
Keywords: privacy notions, membership privacy, differential privacy

July 2012 Proceedings of the VLDB Endowment: Volume 5 Issue 11, July 2012
Publisher: VLDB Endowment
Citation Count: 30
Downloads (6 Weeks): 10,   Downloads (12 Months): 60,   Downloads (Overall): 330

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The discovery of frequent itemsets can serve valuable economic and research purposes. Releasing discovered frequent itemsets, however, presents privacy challenges. In this paper, we study the problem of how to perform frequent itemset mining on transaction databases while satisfying differential privacy. We propose an approach, called PrivBasis, which leverages a ...

6 published by ACM
May 2012 ASIACCS '12: Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security
Publisher: ACM
Citation Count: 18
Downloads (6 Weeks): 9,   Downloads (12 Months): 124,   Downloads (Overall): 655

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This paper aims at answering the following two questions in privacy-preserving data analysis and publishing. The first is: What formal privacy guarantee (if any) does k -anonymization methods provide? k -Anonymization methods have been studied extensively in the database community, but have been known to lack strong privacy guarantees. The ...

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