Data mining is the information technology that extracts valuable knowledge from large amounts of data. Due to the emergence of data streams as a new type of data, data streams mining has recently become a very important and popular research issue. There have been many studies proposing efficient mining algorithms for data streams. On the other hand, data mining can cause a great threat to data ...

Recent advances in hardware technology have increased storage and recording capability with regard to personal data on individuals. This has created fears that such data could be misused. To alleviate such concerns, data was anonymized and many techniques were recently proposed on performing data mining tasks in ways which ensured privacy. Anonymization techniques were drawn from a variety of r...

Data mining is the process of extracting the previously unknown patterns from large amount of data. Privacy preserving data mining is one of the research areas in data mining. The main

Privacy preserving data mining (PPDM) has become a popular research direction in data mining. Privacy preserving data mining is an approach to develop algorithms by which we can modify the utility values of original data using some techniques in order to protect sensitive information from unauthorized user. Protecting data against illegal access becomes a serious issue when this data is require...

Data mining is the process of extracting the previously unknown patterns from large amount of data. Privacy preserving data mining is one of the research areas in data mining. The main

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 to protect the sensitive information in data while extracting knowledge from large amount of data. We focus the general classification in a secured manner and introduce a privacy-p...

As a novel research direction, privacy-preserving data mining (PPDM) has received a great deal of attentions from more and more researchers, and a large number of PPDM algorithms use randomization distortion techniques to mask the data for preserving the privacy of sensitive data. In reality, for PPDM in the data sets, which consist of terabytes or even petabytes of data, efficiency is a paramo...

The perturbation method has been extensively studied for privacy preserving data mining. In this method, random noise from a known distribution is added to the privacy sensitive data before the data is sent to the data miner. Subsequently, the data miner reconstructs an approximation to the original data distribution from the perturbed data and uses the reconstructed distribution for data minin...

To build reliable prediction models and identify useful patterns, assembling data sets from databases maintained by different sources such as hospitals becomes increasingly common; however, it might divulge sensitive information about individuals and thus leads to increased concerns about privacy, which in turn prevents different parties from sharing information. Privacy Preserving Distributed ...