Secure multiparty computation allows multiple parties to participate in a computation. SMC (secure multiparty computation) assumes n parties where n>1. All the parties jointly compute a function. Privacy preserving data mining has become an emerging field in the secure multiparty computation. Privacy preserving data mining preserves the privacy of individual's data. Privacy preserving data mini...

In recent year’s privacy preserving data mining has emerged as a very active research area in data mining. Over the last few years this has naturally lead to a growing interest in security or privacy issues in data mining. More precisely, it became clear that discovering knowledge through a combination of different databases raises important security issues. Privacy preserving data mining is on...

Data mining-as-a-service has been selected as considerable research issue by researchers. An organization (data owner) can outsource its mining needs like resources or expertise to a third party service provider (server). However, both the association rules and the items of the outsourced transaction database are private property of data owner. The data owner encrypts its data, send data and mi...

Privacy-preserving data mining has been the subject of substantial research. This paper summarizes accomplishments, the privacy debate, and outlines areas where privacy issues still impact data mining research and practice.

Privacy preserving association rule mining has triggered the development of many privacy-preserving data mining techniques. A large fraction of them use randomized data distortion techniques to mask the data for preserving. This paper proposes a new transaction randomization method which is a combination of the fake transaction randomization method and a new per-transaction randomization method...

A well known method for privacy-preserving data mining is that of randomization. In randomization, we add noise to the data so that the behavior of the individual records is masked. However, the aggregate behavior of the data distribution can be reconstructed by subtracting out the noise from the data. The reconstructed distribution is often sufficient for a variety of data mining tasks such as...

Peer-to-Peer (P2P) networks are gaining increasing popularity in many distributed applications such as file-sharing, network storage, web caching, searching and indexing of relevant documents and P2P network-threat analysis. Many of these applications require scalable analysis of data over a P2P network. This paper starts by offering a brief overview of distributed data mining applications and ...

As most previous studies on privacy-preserving data mining placed specific importance on the security of massive amounts of data from a static database, consequently data undergoing privacy-preservation often leads to a decline in the accuracy of mining results. Furthermore, following by the rapid advancement of Internet and telecommunication technology, subsequently data types have transformed...

In recent year’s privacy preservation in data mining has become an important issue. A new class of data mining method called privacy preserving data mining algorithm has been developed. The aim of these algorithms is protecting the sensitive information in data while extracting knowledge from large amount of data. The extracted knowledge is generally expressed in the form of cluster, decision t...

In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. A recent framework performs privacy preserving data mining by using a condensation based approach....