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» Smooth sensitivity and sampling in private data analysis
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GRC
2008
IEEE
13 years 8 months ago
Neighborhood Smoothing Embedding for Noisy Manifold Learning
Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
Guisheng Chen, Junsong Yin, Deyi Li
SIGMOD
2007
ACM
144views Database» more  SIGMOD 2007»
14 years 7 months ago
GhostDB: querying visible and hidden data without leaks
Imagine that you have been entrusted with private data, such as corporate product information, sensitive government information, or symptom and treatment information about hospita...
Nicolas Anciaux, Mehdi Benzine, Luc Bouganim, Phil...
FOCS
2008
IEEE
14 years 1 months ago
What Can We Learn Privately?
Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept ...
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi ...
PAKDD
2007
ACM
130views Data Mining» more  PAKDD 2007»
14 years 1 months ago
Deriving Private Information from Arbitrarily Projected Data
Distance-preserving projection based perturbation has gained much attention in privacy-preserving data mining in recent years since it mitigates the privacy/accuracy tradeoff by ac...
Songtao Guo, Xintao Wu
BMCBI
2010
108views more  BMCBI 2010»
13 years 7 months ago
Error, reproducibility and sensitivity : a pipeline for data processing of Agilent oligonucleotide expression arrays
Background: Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much ...
Benjamin Chain, Helen Bowen, John Hammond, Wilfrie...