Preserving Privacy of Temporal Big Data

In the current technological era, huge amounts of big data are generated and collected from a wide variety of rich data sources. Embedded in these big data are useful information and valuable knowledge to be utilized. With the popularity of initiatives of open data, more big data have been published on open data platforms and made accessible to the public. To preserve privacy while maintaining the utility of data, research on privacy-preserving publishing has focused on preserving privacy of sensitive personal data such as patient data for health related applications. However, there are many other real-life situations, in which personal data of individual citizens and their daily routines need to be preserved when publishing. In this paper, we examine the problem of preserving privacy of temporal big data. Specifically, we present a temporal hierarchy privacy preserving model (THPPM) for some common daily routines-for example, parking. The model adapts and extends temporal hierarchy to generalize temporal data related to timestamp and spatial data related to check-in location. It also makes good use of generalized temporal representative points to preserve privacy of specific temporal data points. Evaluations on two real-life datasets on parking tickets for the US city of Buffalo and the Canadian city of Toronto shows that effectiveness and practicality of our THPPM in preserving privacy of temporal big data. Although this model is demonstrated and evaluated on parking ticket data, it would be applicable to preserving privacy of temporal big data for many other real-life applications and services.