State-of-the-art in distributed privacy preserving data mining
Liu Ying-hua, Yang Bing-ru, Cao Dan-yang, Nan Ma · 2011
Privacy preserving data mining has become an important research problem. The chief research is how to mine the potential knowledge and not to reveal the sensitive data. In reality, large amounts of data are stored in distributed sites, so the DPPDM (Distributed Privacy Preserving Data Mining) is very important. This paper gave a survey on the DPPDM. Based on different underlying technologies, there are three kinds of techniques: perturbation, secure multi-party computation and restricted query. It provides a detailed description of the research in this area, compares the advantages and disadvantages of each method, foucs on the hot topic in this field, points out the future research directions.