An Efficient Technique to Secure Data Access Multi Overlapping Slicing : An Efficient Technique to Secure Data Access for Multiple Domains Using Overlapping Slicing

Rani Ingawale

ISBN 10: 6207457242 / ISBN 13: 9786207457243

Published by LAP LAMBERT Academic Publishing

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Data Mining is the process of analyzing data from different perspectives, summarizing it, and extracting the needed information from the database. Most enterprises are collecting and storing data in large databases. Database privacy is an important responsibility of organizations to protect clients sensitive information because client trusts them to do so. Various anonymization techniques have been proposed for the privacy of sensitive microdata. Generalization loses a considerable amount of information, especially for high-dimensional data. Bucketization does not prevent membership disclosure and does not apply to data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. Slicing is a technique proposed for anonymized published datasets by partitioning the dataset vertically and horizontally. The proposed technique increases the utility and privacy of a sliced dataset by allowing overlapped slicing while maintaining the prevention of membership disclosure. It also provides secure data access for multiple domains. This novel approach works on overlapped slicing to improve, preserve data utility and privacy better traditional slicing.

Seller Inventory # 9786207457243

Bibliographic Details

• Title: An Efficient Technique to Secure Data Access Multi Overlapping Slicing : An Efficient Technique to Secure Data Access for Multiple Domains Using Overlapping Slicing
• Publisher: LAP LAMBERT Academic Publishing
• Language: English
• ISBN 10: 6207457242
• ISBN 13: 9786207457243
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 107 grams

About this title

Synopsis

Data Mining is the process of analyzing data from different perspectives, summarizing it, and extracting the needed information from the database. Most enterprises are collecting and storing data in large databases. Database privacy is an important responsibility of organizations to protect clients sensitive information because client trusts them to do so. Various anonymization techniques have been proposed for the privacy of sensitive microdata. Generalization loses a considerable amount of information, especially for high-dimensional data. Bucketization does not prevent membership disclosure and does not apply to data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. Slicing is a technique proposed for anonymized published datasets by partitioning the dataset vertically and horizontally. The proposed technique increases the utility and privacy of a sliced dataset by allowing overlapped slicing while maintaining the prevention of membership disclosure. It also provides secure data access for multiple domains. This novel approach works on overlapped slicing to improve, preserve data utility and privacy better traditional slicing.