Mining Top-k Trajectory-Patterns from Anonymized Data

TL;DR

This paper introduces TopKMintra, a novel method for mining top-k activity-trajectory patterns from anonymized GPS data, overcoming challenges posed by privacy-induced data transformations and ensuring efficient pattern discovery.

Contribution

It proposes a new pattern mining technique that transforms anonymized trajectory data into an intermediate form and restricts pattern extensions to handle 2D data complexities.

Findings

Efficiently mines patterns from anonymized data.

Patterns can be retained and discovered despite anonymization.

Method outperforms existing techniques in effectiveness and efficiency.

Abstract

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.