9786139838233 - Correlation Based Approach for Hiding Sensitive Items in Data Mining: a Novel Approach for Privacy Preserving Data Mining by Sreenivasa Rao, Kuncham; Suresh Babu, C... (5 results)

Taschenbuch. Condition: 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.

Condition: 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.

Taschenbuch. Condition: Neu. Correlation based approach for hiding sensitive items in data mining | A novel approach for Privacy Preserving Data Mining | Kuncham Sreenivasa Rao (u. a.) | Taschenbuch | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139838233 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condition: 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, Überseering 33, 22297 Hamburg 168 pp. Englisch.

Taschenbuch. Condition: 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.