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Privacy Preserving Data Mining - Issues & Techniques: Preserving privacy of data streams and large data sets while mining - Softcover
Inhaltsangabe
Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accuracy of data mining task mainly clustering and classification. Existing techniques for privacy preserving data mining is designed for traditional static data sets and are not suitable for data streams. Privacy preserving data stream mining is an emerging research area in the field of privacy aware data mining.
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Reseña del editor
Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accuracy of data mining task mainly clustering and classification. Existing techniques for privacy preserving data mining is designed for traditional static data sets and are not suitable for data streams. Privacy preserving data stream mining is an emerging research area in the field of privacy aware data mining.
Biografía del autor
Hitesh Chhinkaniwala is a research scholar of Kadi Sarva Vishwavidyalaya, India. He has done B.E. in Computer Engineering in 1997 and M.Tech in Computer Science & Engineering from NIT-Karnataka, India. He is having 15 years of teaching and industry experience. His area of interest is data stream mining, information privacy and statistical analysis.
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Privacy Preserving Data Mining - Issues & Techniques
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chhinkaniwala HiteshHitesh Chhinkaniwala is a research scholar of Kadi Sarva Vishwavidyalaya, India. He has done B.E. in Computer Engineering in 1997 and M.Tech in Computer Science & Engineering from NIT-Karnataka, India. He is havin. Bestandsnummer des Verkäufers 4992818
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Privacy Preserving Data Mining - Issues & Techniques
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Taschenbuch. Zustand: Neu. Neuware -Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accuracy of data mining task mainly clustering and classification. Existing techniques for privacy preserving data mining is designed for traditional static data sets and are not suitable for data streams. Privacy preserving data stream mining is an emerging research area in the field of privacy aware data mining.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 120 pp. Englisch. Bestandsnummer des Verkäufers 9783639510478
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Privacy Preserving Data Mining - Issues & Techniques
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accuracy of data mining task mainly clustering and classification. Existing techniques for privacy preserving data mining is designed for traditional static data sets and are not suitable for data streams. Privacy preserving data stream mining is an emerging research area in the field of privacy aware data mining. 120 pp. Englisch. Bestandsnummer des Verkäufers 9783639510478
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Privacy Preserving Data Mining - Issues & Techniques : Preserving privacy of data streams and large data sets while mining
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accuracy of data mining task mainly clustering and classification. Existing techniques for privacy preserving data mining is designed for traditional static data sets and are not suitable for data streams. Privacy preserving data stream mining is an emerging research area in the field of privacy aware data mining. Bestandsnummer des Verkäufers 9783639510478
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Privacy Preserving Data Mining - Issues & Techniques: Preserving privacy of data streams and large data sets while mining
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Privacy Preserving Data Mining - Issues & Techniques: Preserving privacy of data streams and large data sets while mining
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