Synopsis
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.
"synopsis" may belong to another edition of this title.
About the Author
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.
"About this title" may belong to another edition of this title.
Search results for PPDM using Syntactic Anonymity on Sensitive Data
Seller Image
PPDM using Syntactic Anonymity on Sensitive Data
Published by
LAP LAMBERT Academic Publishing Jul 2017, 2017
ISBN 10: 3330351306
ISBN 13: 9783330351301
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9783330351301
Seller Image
PPDM using Syntactic Anonymity on Sensitive Data
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330351306
ISBN 13: 9783330351301
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: 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. Seller Inventory # 160063928
Buy New
£ 41.09
£ 42.31 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: Over 20 available
Stock Image
PPDM using Syntactic Anonymity on Sensitive Data
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330351306
ISBN 13: 9783330351301
New
Paperback
Seller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Brand New. 140 pages. 8.66x5.91x0.32 inches. In Stock. Seller Inventory # 3330351306
Buy New
£ 83.75
£ 10 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available
Seller Image
PPDM using Syntactic Anonymity on Sensitive Data
Published by
LAP LAMBERT Academic Publishing Jul 2017, 2017
ISBN 10: 3330351306
ISBN 13: 9783330351301
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9783330351301
Seller Image
PPDM using Syntactic Anonymity on Sensitive Data
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330351306
ISBN 13: 9783330351301
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9783330351301
Seller Image
PPDM using Syntactic Anonymity on Sensitive Data
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330351306
ISBN 13: 9783330351301
New
Taschenbuch
Seller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 109610663