Privacy Preservation of Big Spatio-Temporal Co-occurrence Data

For resilient computing in a sustainable cyberphysical world, it is important to well manage data including preserving privacy of data. To elaborate, the terms "terms of use," "public consent," "privacy policy," "reusable data," and "transparency" have gained prominence in relation to the data found on the web, implying that privacy is now a shared responsibility among all parties involved. While privacy remains a concern, the utilization of publicly available data can serve societal interests. For example, incorporating information from emergency calls, substance use, and overdose antagonist drugs can contribute to the development of policies concerning the allocation of emergency resources, distribution of overdose antagonist drugs, and the potential impact on reducing overdose deaths. Hence, in this paper, we explore the privacy preservation while integrating public open data within a temporal and spatial hierarchy. Findings of our evaluation, based on analysis of four open datasets, the effectiveness of our model in privacy preserving record linkage with spatio-temporal hierarchy on co-occurrence data.