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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 /

Contributor(s): Material type: TextTextSeries: Information Systems and Applications, incl. Internet/Web, and HCI ; 4890Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008Edition: 1st ed. 2008Description: IX, 173 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783540784784
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 005.8 23
LOC classification:
  • QA76.9.A25
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 Nature eBook
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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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