Privacy, Security, and Trust in KDD: Second ACM SIGKDD International Workshop, PinKDD 2008, Las Vegas, Nevada, August 24, 2008, Revised by Francesco BonchiPrivacy, Security, and Trust in KDD: Second ACM SIGKDD International Workshop, PinKDD 2008, Las Vegas, Nevada, August 24, 2008, Revised by Francesco Bonchi

Privacy, Security, and Trust in KDD: Second ACM SIGKDD International Workshop, PinKDD 2008, Las…

byFrancesco BonchiEditorElena Ferrari, Wei Jiang

Paperback | May 25, 2009

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This book constitutes the thoroughly refereed post-workshop proceedings of the Second International Workshop on Privacy, Security, and Trust in KDD, PinKDD 2008, held in Las Vegas, NV, USA, in March 2008 in conjunction with the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008. The 5 revised full papers presented together with 1 invited keynote lecture and 2 invited panel sessions were carefully reviewed and selected from numerous submissions. The papers are extended versions of the workshop presentations and incorporate reviewers' comments and discussions at the workshop and represent the diversity of data mining research issues in privacy, security, and trust as well as current work on privacy issues in geographic data mining.
Title:Privacy, Security, and Trust in KDD: Second ACM SIGKDD International Workshop, PinKDD 2008, Las…Format:PaperbackDimensions:127 pages, 23.5 × 15.5 × 1.73 inPublished:May 25, 2009Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3642017177

ISBN - 13:9783642017179

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Table of Contents

Invited Paper.- Data Mining for Security Applications and Its Privacy Implications.- Geocode Matching and Privacy Preservation.- Mobility, Data Mining and Privacy the Experience of the GeoPKDD Project.- Contributed Papers.- Data and Structural k-Anonymity in Social Networks.- Composing Miners to Develop an Intrusion Detection Solution.- Malicious Code Detection Using Active Learning.- Maximizing Privacy under Data Distortion Constraints in Noise Perturbation Methods.- Strategies for Effective Shilling Attacks against Recommender Systems.