In this paper, we investigate how location access patterns influence the re-identification of seemingly anonymous data. In the real world, individuals visit different locations that gather similar information. For instance, multiple hospitals collect health information on the same patient. To protect anonymity for research purposes, hospitals share sensitive data, such as DNA sequences, stripped of explicit identifiers. Separately, for administrative functions, identified data, stripped of DNA, is made available. On a hospital by hospital basis, each pair of DNA and identified databases appears unlinkable, however, links can be established when multiple locations' database are studied. This problem, known as trail re-identification, is a generalized phenomenon and occurs because an individual's location access pattern can be matched across the shared databases. Data holders can not exchange data to find and suppress trails that would be re-identified. Thus, it is important to assess the re-identification risk in a system in order to develop techniques to mitigate it. In this research, we evaluate several real world datasets and observe trail re-identification is related to the number of people to places. To study this phenomenon in more detail, we develop a generative model for location access patterns that simulates observed behavior. We evaluate trail re-identification risk in a range of simulated patterns and our findings suggest that the skew of the distribution of people to places is one of the main factors that drives trail re-identification.
['Bradley Malin', 'Edoardo M. Airoldi']
The effects of location access behavior on re-identification risk in a distributed environment