A MACHINE LEARNING APPROACH FOR PRIVACY PRESERVING LOCATION DATA PUBLISHING (KEY IJP************977)

• Srividya Putty,Roja D,Nagamalleswara Rao Purimetla

Abstract

Now-a-days due to mobile all online applications are recording user locations and then storing them in their apps and these location details can be used to track users. Sometimes some malicious users can track the user location to know where user is travelling to like bank, hospital or any other locations. To overcome this problem and to provide security to user location data many data anonymization techniques such as K-Anonymity and data perturbation are introduced, where Data perturbation will add noise to user data. And K-Anonymity will adjust user data into groups. But above techniques are not reliable because there is a chance of identifying noise data added user locations. To overcome this, there were three important techniques in Machine Learning named Clustering model, Dynamic Sequence Alignment and Data Generalization. Where these models will provide more security and generalize the data which cannot be easily understood to track.