Sinossi
The concern over privacy of personal and sensitive information has led to the implementation of several techniques for hiding, obfuscating, syntactic anonymity and encrypting sensitive information in databases. The requirement of preserving privacy as well as the usability of sensitive data has led to development of nearest neighborhood techniques. In this work we propose a method that expands the scope of perturbation in PPDM as multilevel and multikey trust in privacy preserving data mining. An analogical approach with measuring the identification attacks, diversity attacks and the problem addresses the challenge by properly correlating perturbation across copies of different trust levels and keys that are pertaining to the sub domain contexts of the databases. Our proposed framework is architecturally robust and defends the attacks to achieve the privacy goal. Our framework supports data providers to deliver different forms of data with different privacy levels based on the market demand.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
A.V. Sriharsha, is B.Tech from Computer Science & Engineering from Andhra University and M.Tech from Information Technology from Sathyabhama University, Chennai. Ph.D. from SCSVMV University, Kancheepuram. I am currently working as Professor in the Department of CSE, Sree Vidyanikethan Engineering College, A.Rangampet, Tirupati, A.P.
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PPDM using Syntactic Anonymity on Sensitive Data
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LAP LAMBERT Academic Publishing Jul 2017, 2017
ISBN 10: 3330351306
ISBN 13: 9783330351301
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The concern over privacy of personal and sensitive information has led to the implementation of several techniques for hiding, obfuscating, syntactic anonymity and encrypting sensitive information in databases. The requirement of preserving privacy as well as the usability of sensitive data has led to development of nearest neighborhood techniques. In this work we propose a method that expands the scope of perturbation in PPDM as multilevel and multikey trust in privacy preserving data mining. An analogical approach with measuring the identification attacks, diversity attacks and the problem addresses the challenge by properly correlating perturbation across copies of different trust levels and keys that are pertaining to the sub domain contexts of the databases. Our proposed framework is architecturally robust and defends the attacks to achieve the privacy goal. Our framework supports data providers to deliver different forms of data with different privacy levels based on the market demand. 140 pp. Englisch. Codice articolo 9783330351301
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sriharsha A. V.A.V. Sriharsha, is B.Tech from Computer Science & Engineering from Andhra University and M.Tech from Information Technology from Sathyabhama University, Chennai. Ph.D. from SCSVMV University, Kancheepuram. I am current. Codice articolo 160063928
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PPDM using Syntactic Anonymity on Sensitive Data
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Paperback. Condizione: Brand New. 140 pages. 8.66x5.91x0.32 inches. In Stock. Codice articolo 3330351306
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PPDM using Syntactic Anonymity on Sensitive Data
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LAP LAMBERT Academic Publishing Jul 2017, 2017
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ISBN 13: 9783330351301
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The concern over privacy of personal and sensitive information has led to the implementation of several techniques for hiding, obfuscating, syntactic anonymity and encrypting sensitive information in databases. The requirement of preserving privacy as well as the usability of sensitive data has led to development of nearest neighborhood techniques. In this work we propose a method that expands the scope of perturbation in PPDM as multilevel and multikey trust in privacy preserving data mining. An analogical approach with measuring the identification attacks, diversity attacks and the problem addresses the challenge by properly correlating perturbation across copies of different trust levels and keys that are pertaining to the sub domain contexts of the databases. Our proposed framework is architecturally robust and defends the attacks to achieve the privacy goal. Our framework supports data providers to deliver different forms of data with different privacy levels based on the market demand.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch. Codice articolo 9783330351301
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PPDM using Syntactic Anonymity on Sensitive Data
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The concern over privacy of personal and sensitive information has led to the implementation of several techniques for hiding, obfuscating, syntactic anonymity and encrypting sensitive information in databases. The requirement of preserving privacy as well as the usability of sensitive data has led to development of nearest neighborhood techniques. In this work we propose a method that expands the scope of perturbation in PPDM as multilevel and multikey trust in privacy preserving data mining. An analogical approach with measuring the identification attacks, diversity attacks and the problem addresses the challenge by properly correlating perturbation across copies of different trust levels and keys that are pertaining to the sub domain contexts of the databases. Our proposed framework is architecturally robust and defends the attacks to achieve the privacy goal. Our framework supports data providers to deliver different forms of data with different privacy levels based on the market demand. Codice articolo 9783330351301
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PPDM using Syntactic Anonymity on Sensitive Data
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Taschenbuch. Condizione: Neu. PPDM using Syntactic Anonymity on Sensitive Data | A. V. Sriharsha (u. a.) | Taschenbuch | 140 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330351301 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Codice articolo 109610663
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