Privacy preserving in large databases plays a vital role for the clustering and classification
process. Since, most of the traditional models are difficult to provide privacy on the …

As the size of the privacy preserving databases is increasing, it is difficult to improve the
privacy and accuracy of these databases due to dimensionality and runtime. However, most …

Privacy-preserving data classification is a pervasive task in privacy-preserving data mining
(PPDM). The main goal is to secure the identification of individuals from the released data to …

In this work propose different data perturbation techniques that deal with multi dimensional
data perturbation and achieve high privacy guarantee and zero-loss of accuracy, various …

Data mining is a powerful tool which supports automatic extraction of unknown patterns from
large amounts of data. The knowledge extracted by data mining process support a variety of …

The perturbation technique has been widely considered for privacy preserving in data
mining for different datasets. Generally, Multi-partitioned datasets comprises of both vertical …

Privacy preserving data classification is an important research area in data mining field. The
goal of a privacy preserving classification algorithm is to protect the sensitive information as …

Privacy-preserving in data mining refers to the area of data mining that seeks to safeguard
sensitive information from unsolicited or unsanctioned disclosure and hence protecting …