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Könyv PPDM using Syntactic Anonymity on Sensitive Data A. V. Sriharsha

PPDM using Syntactic Anonymity on Sensitive Data

Szerző: A. V. Sriharsha, C. Parthasarathy
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: LAP Lambert Academic Publishing
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
18 820 Ft
The concern over privacy of personal and sensitive information has led to the implementation of seve...

Információk a könyvről

Szerző
A. V. Sriharsha, C. Parthasarathy
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2017
oldal
140
EAN
9783330351301
Enbook ID
18125192
Kiadó
LAP Lambert Academic Publishing
Súly
227
Méretek
150 x 220 x 8

Teljes leírás

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.

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