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ICDM
2009
IEEE
130views Data Mining» more  ICDM 2009»
13 years 7 months ago
Efficient Anonymizations with Enhanced Utility
The k-anonymization method is a commonly used privacy-preserving technique. Previous studies used various measures of utility that aim at enhancing the correlation between the orig...
Jacob Goldberger, Tamir Tassa
ICDM
2009
IEEE
197views Data Mining» more  ICDM 2009»
13 years 7 months ago
A Linear-Time Graph Kernel
The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures bu...
Shohei Hido, Hisashi Kashima
ICDM
2009
IEEE
172views Data Mining» more  ICDM 2009»
14 years 4 months ago
Fine-Grain Perturbation for Privacy Preserving Data Publishing
— Recent work [12] shows that conventional privacy preserving publishing techniques based on anonymity-groups are susceptible to corruption attacks. In a corruption attack, if th...
Rhonda Chaytor, Ke Wang, Patricia Brantingham
ICDM
2009
IEEE
172views Data Mining» more  ICDM 2009»
14 years 4 months ago
Sparse Least-Squares Methods in the Parallel Machine Learning (PML) Framework
—We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classificatio...
Ramesh Natarajan, Vikas Sindhwani, Shirish Tatikon...
ICDM
2009
IEEE
223views Data Mining» more  ICDM 2009»
14 years 4 months ago
Differential Privacy for Clinical Trial Data: Preliminary Evaluations
—The concept of differential privacy as a rigorous definition of privacy has emerged from the cryptographic community. However, further careful evaluation is needed before we ca...
Duy Vu, Aleksandra Slavkovic