Privacy in spatiotemporal data mining

Spatio-temporal data due to its time and space dimensions is highly vulnerable to misuse. In fact, one of the limitations for the deployment of Location Based Services is privacy concerns. In order to avoid the privacy threats, one approach would be to suppress the identities of individuals before the data is released. Unfortunately this is not enough since spatio-temporal trajectories can easily be linked to individuals using publicly available information such as home and work addresses. Therefore, new techniques for de-identifying, or anonymizing spatio-temporal data is needed if the data is going to be handed over to a third party. Spatio-temporal data anonymization was addressed in Chapter 1. In addition to that, we need to develop privacy preserving data mining techniques. Time-stamped location observations of an object can not be regarded as normal tabular data since spatio-temporal observations of an object are not independent. Therefore employing the existing privacy preserving data mining techniques as they are would not be enough to solve our problem. Trajectories, instead of plain spatio-temporal observations need to be considered from the privacy perspective. Trajectories and trajectory databases are explained in Chapter X. In this chapter, we will concentrate on the previously proposed methods on privacy preserving data mining and provide a road-map for the privacy preserving spatio-temporal data mining methods.