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Privacy, Security, and Trust in KDD [electronic resource] : First ACM SIGKDD International Workshop, PinKDD 2007, San Jose, CA, USA, August 12, 2007, Revised Selected Papers / edited by Francesco Bonchi, Elena Ferrari, Bradley Malin, Yücel Saygin.

Contributor(s): Material type: TextTextSeries: Lecture Notes in Computer Science ; 4890Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008Description: online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783540784784
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 005.7 23
LOC classification:
  • QA76.76.A65
Online resources:
Contents:
Invited Paper -- An Ad Omnia Approach to Defining and Achieving Private Data Analysis -- Contributed Papers -- Phoenix: Privacy Preserving Biclustering on Horizontally Partitioned Data -- Allowing Privacy Protection Algorithms to Jump Out of Local Optimums: An Ordered Greed Framework -- Probabilistic Anonymity -- Website Privacy Preservation for Query Log Publishing -- Privacy-Preserving Data Mining through Knowledge Model Sharing -- Privacy-Preserving Sharing of Horizontally-Distributed Private Data for Constructing Accurate Classifiers -- Towards Privacy-Preserving Model Selection -- Preserving the Privacy of Sensitive Relationships in Graph Data.
In: Springer eBooks
Item type: E-BOOKS
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IMSc Library Link to resource Available EBK8033

Invited Paper -- An Ad Omnia Approach to Defining and Achieving Private Data Analysis -- Contributed Papers -- Phoenix: Privacy Preserving Biclustering on Horizontally Partitioned Data -- Allowing Privacy Protection Algorithms to Jump Out of Local Optimums: An Ordered Greed Framework -- Probabilistic Anonymity -- Website Privacy Preservation for Query Log Publishing -- Privacy-Preserving Data Mining through Knowledge Model Sharing -- Privacy-Preserving Sharing of Horizontally-Distributed Private Data for Constructing Accurate Classifiers -- Towards Privacy-Preserving Model Selection -- Preserving the Privacy of Sensitive Relationships in Graph Data.

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The Institute of Mathematical Sciences, Chennai, India