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KDD
2004
ACM
160views Data Mining» more  KDD 2004»
14 years 8 months ago
k-TTP: a new privacy model for large-scale distributed environments
Secure multiparty computation allows parties to jointly compute a function of their private inputs without revealing anything but the output. Theoretical results [2] provide a gen...
Bobi Gilburd, Assaf Schuster, Ran Wolff
ICDE
2005
IEEE
183views Database» more  ICDE 2005»
14 years 1 months ago
Protection of Location Privacy using Dummies for Location-based Services
Recently, highly accurate positioning devices enable us to provide various types of location-based services. On the other hand, because position data obtained by such devices incl...
Hidetoshi Kido, Yutaka Yanagisawa, Tetsuji Satoh
ICDM
2010
IEEE
160views Data Mining» more  ICDM 2010»
13 years 6 months ago
A Privacy Preserving Framework for Gaussian Mixture Models
Abstract--This paper presents a framework for privacypreserving Gaussian Mixture Model computations. Specifically, we consider a scenario where a central service wants to learn the...
Madhusudana Shashanka
CORR
2010
Springer
134views Education» more  CORR 2010»
13 years 8 months ago
Large Margin Multiclass Gaussian Classification with Differential Privacy
As increasing amounts of sensitive personal information is aggregated into data repositories, it has become important to develop mechanisms for processing the data without revealin...
Manas A. Pathak, Bhiksha Raj
AUSDM
2007
Springer
165views Data Mining» more  AUSDM 2007»
14 years 1 days ago
A New Efficient Privacy-Preserving Scalar Product Protocol
Recently, privacy issues have become important in data analysis, especially when data is horizontally partitioned over several parties. In data mining, the data is typically repre...
Artak Amirbekyan, Vladimir Estivill-Castro