PRIVACY PRESERVING LOCATION DATA PUBLISHING USING S-MLA

Author (s) : A.Jothimurugan &M.Punitha, Mrs.A.Akthar Ruksana &Dr.K.Swathi

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Abstract :
Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users’ private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this project, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as signature based machine learning based anonymization (S-MLA). S-MLA consists of 3 interworking algorithms: digital signature generation, clustering and alignment. Alignment : By formulating the anonymization process as an optimization problem and finding an alternative representation of the system’ Clustering.

Keywords : Publishing datasets, S-MLA, digital signature generation, clustering and alignment

Conference Date : 17/09/2021

Pages : 228

Paper ID : IESMDT226