Data Restoration and Privacy Preserving of Data Mining Using Random Decision Tree Over Vertically Partitioned Data (International Journal of Engineering and Computer Science, 2017)
In recent years with the development of network data collection and storage technology, the usage and sharing of large amounts of data has become easy process. Once the data and information is accumulated, its will become the wealth of information. Data mining, otherwise known as knowledge discovery, can extracted meaningful information from the large amounts of data because it supports people’s decision- making. However the traditional data mining techniques and algorithms directly operated on the original data set, which will cause the leakage of privacy data, these problems challenge the traditional data mining, so privacy-preserving data mining (PPDM) it has become one of the most newest trends in the privacy, security and data mining research. Existing cryptography is the based work for privacy-preserving data mining and  is still too slow to be effective for the large scale. But proposed approach is based on exploit for fact that RDTs can naturally it  fit into the  parallel and fully distributed architecture.