  The ubiquity of GPS enabled devices result into the generation of an enormous
amount of user movement data consisting of a sequence of locations together
with activities performed at those locations. Such data, commonly known as {\it
activity-trajectory data}, can be analysed to find common user movements and
preferred activities, which will have tremendous business value. However,
various countries have now introduced data privacy regulations that make it
mandatory to anonymize any data before releasing it. This makes it difficult to
look for patterns as the existing mining techniques may not be directly
applicable over anonymized data. User locations in an activity-trajectory
dataset are anonymized to regions of different shapes and sizes making them
uncomparable; therefore, it is unclear how to define suitable patterns over
those regions. In this paper, we propose a top-k pattern mining technique
called TopKMintra that employs a pre-processing step to transform anonymized
activity-trajectory into an intermediate representation that address the above
problem. Unlike usual sequential data, activity-trajectory data is
2-dimensional that can lead to generation of duplicate patterns. To handle
this, TopKMintra restricts arbitrary extensions in the two dimensions by
imposing an order over the search space; this also helps in addressing the
common problem of updating the threshold in top-k pattern mining algorithms
during various stages. We perform extensive experiments on real datasets to
demonstrate the efficiency and the effectiveness of TopKMintra. Our results
show that even after anonymization, certain patterns may remain in a dataset
and those could be mined efficiently.
