Privacy, Security, and Trust in KDD

This book constitutes the post-workshop proceedings of the First International Workshop on Privacy, Security, and Trust in KDD. It covers all prevailing topics concerning privacy, security, and trust aspects of data mining and knowledge discovery.

Inhalt

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.