Anonymizing k-NN Classification on MapReduce

Data analytics scenario such as a classification algorithm plays an important role in data mining to identify a category of a new observation and is often used to drive new knowledge. However, classification algorithm on a big data analytics platform such as MapReduce and Spark, often runs on plain text without an appropriate privacy protection mechanism. This leaves user's data to be vulnerable from unauthorized access and puts the data at a great privacy risk. To address such concern, we propose a new novel k-NN classifier which can run on an anonymized dataset on MapReduce platform. We describe new Map and Reduce algorithms to produce different anonymized datasets for k-NN classifier. We also illustrate the details of experiments we performed on the multiple anonymized data sets to understand the effects between the level of privacy protection (data privacy) and the high-value insights (data utility) trade-off before and after data anonymization.