Privacy-preserving classification is the task of learning or training a classifier on the union of privately distributed datasets without sharing the datasets. The emphasis of existing studies in privacy-preserving classification has primarily been put on the design of privacy-preserving versions of particular data mining algorithms, However, in classification problems, preprocessing and postpr...

Data mining is the process of extraction of data from large database. One of the most important topics in research community is Privacy preserving data mining (PPDM). Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. It is essential to maintain a ratio between privacy protection and knowledge discovery. To s...

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer...

Often, the information is sensitive or private in nature and these sensitive data when mined violates the privacy of the individuals. Privacy preserving data mining (PPDM) mines the data but intends to preserve the privacy of susceptible data without ever actually seeing it. This paper recaps the important techniques in PPDM like anonymization, perturbation and cryptography. Nowadays, data mini...

With the rapid development of data mining technologies, preserving privacy in certain data becomes a challenge to data mining applications in many fields, especially in medical, financial and homeland security fields. We present a class of novel privacy-preserving data distortion methods in collaborative analysis situations based on wavelet transformation, to keep the data privacy and data stat...

Privacy-preserving data mining has become an important topic, and many methods have been proposed for a diverse set of privacy-preserving data mining tasks. However, privacy-preserving decision tree mining pioneered by [1] still remains to be elusive. Indeed, the work of [1] was recently showed to be awed [2], meaning that an adversary can actually recover the original data from the perturbed o...

Private data is commonly revealed to the party performing the computation on it. This poses a problem, particularly when outsourcing storage and computation, e.g., to the cloud. In this paper we present a review of security mechanisms and a research agenda for privacypreserving computation. We begin by reviewing current application scenarios where computation faces privacy requirements. We then...