Studies of cellular-network data, comprising analyses of user movements across the network, are becoming increasingly popular with the rise of new services based on user behavior and geographic location. Such data might contain, for example, timestamped lists of IDs of cells a user was registered to. If coupled with the cell locations, confidential to the operator, this would enable reconstruction of user trajectories across the regions covered. It is vital to preserve privacy of this data for all parts involved (users, operators) while enabling open sharing of the data to foster research and development of new findings, services and technologies based on mining of this data. Thus, we pose the problem of obfuscating entire cellular-network topologies while retaining some of the analytical value of the user data. To this end, we adopt multiple obfuscation methods based on different topology-graph distortions, and analyze their performance using simulated scenarios.

With global mobile phone penetration nearing 100%, cellular Call Data Records (CDRs) provide a large-scale and ubiquitous, but also sparse and skewed snapshot of human mobility. It may be difficult or inappropriate to reach strong conclusions about user movement based on such data without proper understanding of user movement between call records. Based on an analysis of a real-world trace, we propose a novel, probabilistic Inter-Call Mobility (ICM) model of users' position in between calls. The ICM model combines Gaussian mixtures to build a general, comprehensive spatio-temporal refinement of CDRs.We demonstrate that ICM model's application yields strikingly different conclusions to the existing models when applied to basic CDR analyses, such as user proximity probability.

Data captured from a live cellular network with the real users during their common daily routine help to understand how the users move within the network. Unlike the simulations with limited potential or expensive experimental studies, the research in user-mobility or spatio-temporal user behavior can be conducted on publicly available datasets such as the Reality Mining Dataset. These data have been for many years a source of valuable information about social interconnection between users and user-network associations. However, an important, spatial dimension is missing in this dataset. In this paper, we present a methodology for retrieving geographical locations matching the GSM cell identifiers in the Reality Mining Dataset, an approach base on querying the Google Location API. A statistical analysis of the measure of success of locations retrieval is provided. Further, we present the LAC-clustering method for detecting and removing outliers, a heuristic extension of general agglomerative hierarchical clustering. This methodology enables further, previously impossible analysis of the Reality Mining Dataset, such as studying user mobility patterns, describing spatial trajectories and mining the spatio-temporal data.

We present the SS7Box modular signaling platform, a tool for rapid application prototyping in a cellular mobile network, and examine in detail its performance limits for the application of active network-based tracking, called SS7Tracker. This application is a highly configurable, non-intrusive and cost-effective solution for large-scale data collection on user mobility in the network, uniquely enabling tracking of both active and passive mobile clients. We present performance studies of real deployment in an existing cellular network and document the measured as well as simulated performance limits such as platform interconnection utilization. Other factors, such as impact on battery consumption of the tracked device, are studied as well. Platform modularity and variability is discussed and demonstrated by further deployed use cases. We conclude by observing promising applicability for future cellular networks.

The mobile communication industry has experienced huge growth in recent years, which amplified the competition among telecommunication companies. This inspired the search for new means of GSM network optimization that would bring better signal coverage and thus competitive advantage. One of the possibilities is active tracking of inroamer location in order to find where they disappear to a rival network. Data collected in this way can then be analyzed to detect weak points of network or traffic anomalies. This paper presents our implementation of active tracking of inroamer location within a live GSM network and the subsequent analysis of collected data, together with future plans for our platform called SS7Tracker.