Location Based Privacy Preserving using Machine Learning

Abstract

Location-based privacy preservation is crucial in the digital age due to the widespread use of location-based services and growing concerns about individual privacy. Despite the use of k-anonymity measures, current systems face challenges, particularly the risk of re-identification, especially when attackers have additional contextual information. These systems also suffer from information loss, leading to a significant decrease in data utility. Striking the right balance between privacy and data utility remains a prominent challenge in the field of location-based privacy preservation. To address the existing gaps and challenges in privacy-preserving location data publishing, a robust framework termed Location anonymization framework is being introduced which consists of three methods Generalization, Sequence alignment and Clustering. DGH Trees are being used to minimize the information loss and also maintains a balance between privacy and utility. To enhance privacy preservation in overly sensitive datasets, a modified version of the k-means algorithm is being put forth. Moreover, to improve the alignment process, the more efficient iterative multisequence alignment is being opted for over the progressive counterpart within this framework. The aim is to achieve a more balanced trade-off between privacy and utility in location data publishing.