Abstract:
Data mining is the pulling out of veiled data from large database. The main problem
that rises in any bulk group of data is that of secrecy of the data. Privacy-preserving in
data mining (PPDM) is the part of data mining that pursues to protect sensitive information from unwanted or illegal disclosure. The attributes are segregated based
on their sensitivity for privacy preservation purposes. Automating this attribute
segregation becomes complicated in high dimensional datasets and data streams.
Feature selection is an important aspect in privacy preserving. In the Existing System,
information or correlation of the attribute on the target class attribute is measured
using Information Gain [IG], Gain Ratio [GR] and Pearson Correlation [PC] ranker
based feature selection methods with decision tree and this values are used to
segregate them as Sensitive Attributes [SA], Quasi Identifiers [QI] and Non-Sensitive.
Segregated attributes are subjected to various levels of privacy preservation using
both the Double layer Perturbation [DLP] and Single Layer Perturbation [SLP]
algorithms to form the perturbed datasets. Since the attribute selection uses tree
structure in methods like IG and GR for each attribute value selection in multiple passes, its time complexity is higher. The dissertation work proposes to use the linked
array for calculating information gain that would involve only one-pass reading of the
dataset and hence increase the efficiency.
