Big data has brought great access and utilization of enormous volumes of data that have become essential for research, legislation, and corporate choices. Open data projects have made it easier for many datasets to be shared, therefore greatly advancing science and public openness. Early projects in the 1990s marked the beginning of open data; it then acquired great pace in the 2000s and 2010s as governments, companies, and scholars realized the need of freely sharing data. The complexity of the data being exchanged grew as technology developed, which created need for increasingly advanced techniques to strike a compromise between privacy and openness. But as datasets get more detailed—especially with regard to spatiotemporal trajectories—ensuring user privacy becomes increasingly difficult. In the field of open data research, a major difficulty is the capacity to anonymize these datasets thereby preserving their usefulness. Sensitive information in datasets was historically safeguarded by either deleting or encrypting identities. But it became clear from the development of sophisticated data analysis approaches that more solid solutions were required to guarantee privacy. Conventional systems sometimes fell short in sufficiently handling the complexity of spatiotemporal datasets. Therefore, by offering a creative solution to the difficulty of anonymizing spatiotemporal trajectory information, this research suggests an original technique like the Machine Learning-based Anonymization (MLA) framework that answers a vital requirement in the open data world. It helps much to ensure ethical use of open data for research and analysis by balancing data utility with user privacy.

privacy-preserving, spatiotemporal, machine learning-based anonymization.