Correlation based approach for hiding sensitive items in data mining: A novel approach for Privacy Preserving Data Mining - Couverture souple

Sreenivasa Rao, Kuncham ; Suresh Babu, Changalasetty ; Damodaram, Avula

9786139838233: Correlation based approach for hiding sensitive items in data mining: A novel approach for Privacy Preserving Data Mining

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ISBN 10 : 6139838231 ISBN 13 : 9786139838233

Editeur : LAP LAMBERT Academic Publishing, 2018

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2018
Langue: anglais
ISBN 10: 6139838231
ISBN 13: 9786139838233
Reliure: Broch�
Nombre de pages: 168
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Correlation based approach for hiding sensitive items in data mining

Kuncham Sreenivasa Rao

Edit� par LAP LAMBERT Academic Publishing Mai 2018, 2018

ISBN 10 : 6139838231 ISBN 13 : 9786139838233

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The main goal of data mining is to extract high level or hidden information from large databases. Along with the advantage of extracting useful pattern, it also poses threats of revealing user�s sensitive information. We can hide sensitive information of the user by using privacy preservation data mining(PPDM). In data mining, association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. As association rule is a key tool for finding such patterns, certain association rules can be categorized as sensitive if its disclosure risk is above some given specified threshold. Most privacy preserving data mining approaches use support and confidence. Author in this book proposed correlation based approach which uses measures other than support and confidence such as correlation among items in sensitive itemsets to hide the sensitive frequent itemsets. Columns in dataset having a specified correlation threshold value are considered for hiding process. This mechanism is called Pearson�s correlation coefficient weighing mechanism which maintains the trade off between privacy and acuuracy. 168 pp. Englisch. N� de r�f. du vendeur 9786139838233

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Correlation based approach for hiding sensitive items in data mining: A novel approach for Privacy Preserving Data Mining

Sreenivasa Rao, Kuncham; Suresh Babu, Changalasetty; Damodaram, Avula

Edit� par LAP LAMBERT Academic Publishing, 2018

ISBN 10 : 6139838231 ISBN 13 : 9786139838233

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Correlation based approach for hiding sensitive items in data mining

Kuncham Sreenivasa Rao|Changalasetty Suresh babu|Avula Damodaram

Edit� par LAP LAMBERT Academic Publishing, 2018

ISBN 10 : 6139838231 ISBN 13 : 9786139838233

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sreenivasa Rao KunchamKuncham Sreenivasa Rao has done his B.Tech in Computer Science and Engineering (CSE) from JNT University, Hyderabad in the year 2005, M.Tech in CSE from JNT University Kakinada in the year 2009. He Obtained his. N� de r�f. du vendeur 504548460

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Correlation based approach for hiding sensitive items in data mining: A novel approach for Privacy Preserving Data Mining

Sreenivasa Rao, Kuncham; Suresh Babu, Changalasetty; Damodaram, Avula

Edit� par LAP LAMBERT Academic Publishing, 2018

ISBN 10 : 6139838231 ISBN 13 : 9786139838233

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Correlation based approach for hiding sensitive items in data mining: A novel approach for Privacy Preserving Data Mining

Sreenivasa Rao, Kuncham; Suresh Babu, Changalasetty; Damodaram, Avula

Edit� par LAP LAMBERT Academic Publishing, 2018

ISBN 10 : 6139838231 ISBN 13 : 9786139838233

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Correlation based approach for hiding sensitive items in data mining

Kuncham Sreenivasa Rao

Edit� par LAP LAMBERT Academic Publishing Mai 2018, 2018

ISBN 10 : 6139838231 ISBN 13 : 9786139838233

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The main goal of data mining is to extract high level or hidden information from large databases. Along with the advantage of extracting useful pattern, it also poses threats of revealing user�s sensitive information. We can hide sensitive information of the user by using privacy preservation data mining(PPDM). In data mining, association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. As association rule is a key tool for finding such patterns, certain association rules can be categorized as sensitive if its disclosure risk is above some given specified threshold. Most privacy preserving data mining approaches use support and confidence. Author in this book proposed correlation based approach which uses measures other than support and confidence such as correlation among items in sensitive itemsets to hide the sensitive frequent itemsets. Columns in dataset having a specified correlation threshold value are considered for hiding process. This mechanism is called Pearson�s correlation coefficient weighing mechanism which maintains the trade off between privacy and acuuracy.Books on Demand GmbH, �berseering 33, 22297 Hamburg 168 pp. Englisch. N� de r�f. du vendeur 9786139838233

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Correlation based approach for hiding sensitive items in data mining : A novel approach for Privacy Preserving Data Mining

Kuncham Sreenivasa Rao

Edit� par LAP LAMBERT Academic Publishing, 2018

ISBN 10 : 6139838231 ISBN 13 : 9786139838233

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The main goal of data mining is to extract high level or hidden information from large databases. Along with the advantage of extracting useful pattern, it also poses threats of revealing user�s sensitive information. We can hide sensitive information of the user by using privacy preservation data mining(PPDM). In data mining, association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. As association rule is a key tool for finding such patterns, certain association rules can be categorized as sensitive if its disclosure risk is above some given specified threshold. Most privacy preserving data mining approaches use support and confidence. Author in this book proposed correlation based approach which uses measures other than support and confidence such as correlation among items in sensitive itemsets to hide the sensitive frequent itemsets. Columns in dataset having a specified correlation threshold value are considered for hiding process. This mechanism is called Pearson�s correlation coefficient weighing mechanism which maintains the trade off between privacy and acuuracy. N� de r�f. du vendeur 9786139838233

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