A privacy-aware trajectory tracking query engine

Abstract

Advances in telecommunications and GPS sensors technology have made possible the collection of data like time series of locations, related to the movement of individuals. The analysis of this, so-called trajectory data, is beneficial both for the individuals (e.g., through location-based services) and for the community as a whole (e.g., decision support for urban planning or traffic control). However, because of the very nature of this data, strict safeguards must be enforced to ensure the privacy of the individuals, whose movement is recorded. In this paper, we present a privacy-aware trajectory tracking query engine that offers strict guarantees about what can be observed by untrusted third parties. Through the query engine, subscribed users can gain restricted access to an in-house trajectory data warehouse, to perform certain analysis tasks. In addition to regular queries involving non-spatial non-temporal attributes, the engine supports a variety of spatiotemporal queries, including range queries, nearest neighbor queries and queries for aggregate statistics. The query results are augmented with fake trajectory data (dummies) to fulfil the requirements of K -anonymity. Through qualitative analysis, we prove the effectiveness of our approach towards blocking certain types of attacks, while minimally distorting the dataset.