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» A Clustering Approach for Achieving Data Privacy
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PAKDD
2009
ACM
123views Data Mining» more  PAKDD 2009»
13 years 12 months ago
Clustering with Lower Bound on Similarity
We propose a new method, called SimClus, for clustering with lower bound on similarity. Instead of accepting k the number of clusters to find, the alternative similarity-based app...
Mohammad Al Hasan, Saeed Salem, Benjarath Pupacdi,...
ICDE
2007
IEEE
115views Database» more  ICDE 2007»
14 years 8 months ago
MultiRelational k-Anonymity
k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. Much...
Mehmet Ercan Nergiz, Chris Clifton, A. Erhan Nergi...
FAST
2011
12 years 11 months ago
Tradeoffs in Scalable Data Routing for Deduplication Clusters
As data have been growing rapidly in data centers, deduplication storage systems continuously face challenges in providing the corresponding throughputs and capacities necessary t...
Wei Dong, Fred Douglis, Kai Li, R. Hugo Patterson,...
ICCCN
2007
IEEE
14 years 1 months ago
Large-Scale QoS-Aware Service-Oriented Networking with a Clustering-Based Approach
—Motivated by the fact that most of the existing QoS service composition solutions have limited scalability, we develop a hierarchical-based solution framework to achieve scalabi...
Jingwen Jin, Jin Liang, Jingyi Jin, Klara Nahrsted...
POPL
2012
ACM
12 years 2 months ago
Probabilistic relational reasoning for differential privacy
Differential privacy is a notion of confidentiality that protects the privacy of individuals while allowing useful computations on their private data. Deriving differential priva...
Gilles Barthe, Boris Köpf, Federico Olmedo, S...