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Association Rule Hiding for Data Mining [recurso electrónico] / by Aris Gkoulalas-Divanis, Vassilios S. Verykios.

Por: Gkoulalas-Divanis, Aris [author.].
Colaborador(es): Verykios, Vassilios S [author.] | SpringerLink (Online service).
Tipo de material: materialTypeLabelLibroSeries Advances in Database Systems: 41Editor: Boston, MA : Springer US, 2010Descripción: XX, 138p. 120 illus., 60 illus. in color. online resource.Tipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9781441965691.Tema(s): Computer science | Operating systems (Computers) | Data structures (Computer science) | Computer software | Database management | Information systems | Artificial intelligence | Computer Science | Database Management | Information Systems Applications (incl.Internet) | Artificial Intelligence (incl. Robotics) | Data Structures, Cryptology and Information Theory | Algorithm Analysis and Problem Complexity | Performance and ReliabilityFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 005.74 Recursos en línea: Libro electrónico
Contenidos:
Fundamental Concepts -- Background -- Classes of Association Rule Hiding Methodologies -- Other Knowledge Hiding Methodologies -- Summary -- Heuristic Approaches -- Distortion Schemes -- Blocking Schemes -- Summary -- Border Based Approaches -- Border Revision for Knowledge Hiding -- BBA Algorithm -- Max-Min Algorithms -- Summary -- Exact Hiding Approaches -- Menon's Algorithm -- Inline Algorithm -- Two-Phase Iterative Algorithm -- Hybrid Algorithm -- Parallelization Framework for Exact Hiding -- Quantifying the Privacy of Exact Hiding Algorithms -- Summary -- Epilogue -- Conclusions -- Roadmap to Future Work.
En: Springer eBooksResumen: Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique on data mining, which studies the problem of hiding sensitive association rules from within the data. Association Rule Hiding for Data Mining addresses the optimization problem of “hiding” sensitive association rules which due to its combinatorial nature admits a number of heuristic solutions that will be proposed and presented in this book. Exact solutions of increased time complexity that have been proposed recently are also presented as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a discussion regarding unsolved problems and future directions. Specific examples are provided throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem. Association Rule Hiding for Data Mining is designed for researchers, professors and advanced-level students in computer science studying privacy preserving data mining, association rule mining, and data mining. This book is also suitable for practitioners working in this industry.
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Tipo de ítem Ubicación actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos QA76.9 .D3 (Navegar estantería) 1 No para préstamo 371695-2001

Fundamental Concepts -- Background -- Classes of Association Rule Hiding Methodologies -- Other Knowledge Hiding Methodologies -- Summary -- Heuristic Approaches -- Distortion Schemes -- Blocking Schemes -- Summary -- Border Based Approaches -- Border Revision for Knowledge Hiding -- BBA Algorithm -- Max-Min Algorithms -- Summary -- Exact Hiding Approaches -- Menon's Algorithm -- Inline Algorithm -- Two-Phase Iterative Algorithm -- Hybrid Algorithm -- Parallelization Framework for Exact Hiding -- Quantifying the Privacy of Exact Hiding Algorithms -- Summary -- Epilogue -- Conclusions -- Roadmap to Future Work.

Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique on data mining, which studies the problem of hiding sensitive association rules from within the data. Association Rule Hiding for Data Mining addresses the optimization problem of “hiding” sensitive association rules which due to its combinatorial nature admits a number of heuristic solutions that will be proposed and presented in this book. Exact solutions of increased time complexity that have been proposed recently are also presented as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a discussion regarding unsolved problems and future directions. Specific examples are provided throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem. Association Rule Hiding for Data Mining is designed for researchers, professors and advanced-level students in computer science studying privacy preserving data mining, association rule mining, and data mining. This book is also suitable for practitioners working in this industry.

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