Privacy preservation is the data mining technique which is to be applied on the databases without violating the privacy of individuals. The sensitive attribute can be selected from the numerical data and it can be modified by any data modification technique. After modification, the modified data can be released to any agency. If they can apply data mining techniques such as clustering, classification etc for data analysis, the modified data does not affect the result. In privacy preservation technique, the sensitive data is converted into modified data using S-shaped fuzzy membership function. K-means clustering is applied for both original and modified data to get the clusters. t-closeness requires that the distribution of sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table. Earth Mover Distance (EMD) is used to measure the distance between the two distributions should be no more than a threshold t. Hence privacy is preserved and accuracy of the data is maintained.
Better embeddings for planar Earth-Mover Distance over sparse sets
