Privacy Preserving Location Data Publishing: A Machine Learning Approach

Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such publication might reveal users' private information. One the most sensitive sources is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve privacy users. Adversaries may know parts trajectories or be able to link published dataset other for purpose user identification. Therefore, it crucial apply preserving techniques before In this paper, we propose a robust framework anonymization termed as machine learning based (MLA). By introducing new formulation problem, are algorithms clustering use k-means algorithm purpose. A variation also proposed overly Moreover, improve alignment process by considering multiple sequence part MLA. The all applied T-Drive, Geolife, Gowalla location experimental results indicate significantly higher utility on MLA framework.