GLOVE: towards privacy-preserving publishing of record-level-truthful mobile phone trajectories

Datasets of mobile phone trajectories collected by network operators offer an unprecedented opportunity to discover new knowledge from the activity of large populations of millions. However, publishing such trajectories also raises significant privacy concerns, as they contain personal data in the form of individual movement patterns. Privacy risks induce network operators to enforce restrictive confidential agreements in the rare occasions when they grant access to collected trajectories, whereas a less involved circulation of these data would fuel research and enable reproducibility in many disciplines. In this work, we contribute a building block toward the design of privacy-preserving datasets of mobile phone trajectories that are truthful at the record level. We present GLOVE, an algorithm that implements k-anonymity, hence solving the crucial unicity problem that affects this type of data while ensuring that the anonymized trajectories correspond to real-life users. GLOVE builds on original insights about the root causes behind the undesirable unicity of mobile phone trajectories, and leverages generalization and suppression to remove them. Proof-of-concept validations with large-scale real-world datasets demonstrate that the approach adopted by GLOVE allows preserving a substantial level of accuracy in the data, higher than that granted by previous methodologies.This work was supported by the Atracción de Talento Investigador program of the Comunidad de Madrid under Grant No. 2019-T1/TIC-16037 NetSense

A Comparative Evaluation of Urban Fabric Detection Techniques Based on Mobile Traffic Data

International audienceMobile traffic data has been recently used to characterize the urban environment in terms of urban fabric profiles. While showing promising results, the existing urban fabric detection solutions are built without a clear understanding of the detection process chain. In this paper, we distinguish and analyze the different steps common to all urban profiling techniques. By evaluating the impact of each step of the process, we are able to propose a new solution that outperforms the state of the art techniques. Our approach uses the weekly periodicity of human activities, as well as a median-based filtering technique, resulting in a better clustering in terms of both coverage and entropy, as shown by results obtained on two large scale mobile traffic datasets covering the urban areas of Milan and Turin, in Italy

Joint Spatial and Temporal Classification of Mobile Traffic Demands

International audienceMobile traffic data collected by network operators is a rich source of information about human habits, and its analysis provides insights relevant to many fields, including urbanism, transportation, sociology and networking. In this paper, we present an original approach to infer both spatial and temporal structures hidden in the mobile demand, via a first-time tailoring of Exploratory Factor Analysis (EFA) techniques to the context of mobile traffic datasets. Casting our approach to the time or space dimensions of such datasets allows solving different problems in mobile traffic analysis, i.e., network activity profiling and land use detection, respectively. Tests with real-world mobile traffic datasets show that, in both its variants above, the proposed approach (i) yields results whose quality matches or exceeds that of state-of-the-art solutions, and (ii) provides additional joint spatiotemporal knowledge that is critical to result interpretation

Characterizing and Removing Oscillations in Mobile Phone Location Data

IEEE WoWMoM 2019, 20th IEEE International symposium on a World of Wireless, Mobile and Multimedia Networks, Washington, ETATS-UNIS, 10-/06/2019 - 12/06/2019International audienceHuman mobility analysis is a multidisciplinary research subject that has attracted a growing interest over the last decade. A substantial amount of such recent studies is driven by the availability of original sources of real-world information about individual movement patterns. An important task in the analysis of mobility data is reliably distinguishing between the stop locations and movement phases that compose the trajectories of the monitored subjects. The problem is especially challenging when mobility is inferred from mobile phone location data: here, oscillations in the association of mobile devices to base stations lead to apparent user mobility even in absence of actual movement. In this paper, we leverage a unique dataset of spatiotemporal individual trajectories that allows capturing both the user and network operator perspectives in mobile phone location data, and investigate the oscillation phenomenon. We present probabilistic and machine learning approaches for detecting oscillations in mobile phone location data, and a filtering technique for removing those. Our analyses and comparison with state-of-the-art approaches demonstrate the superiority of our solution, both in terms of removed oscillations and of error with respect to ground-truth trajectories

Mobile Demand Profiling for Cellular Cognitive Networking

International audienceIn the next few years, mobile networks will undergo significant evolutions in order to accommodate the ever-growing load generated by increasingly pervasive smartphones and connected objects. Among those evolutions, cognitive networking upholds a more dynamic management of network resources that adapts to the significant spatiotemporal fluctuations of the mobile demand. Cognitive networking techniques root in the capability of mining large amounts of mobile traffic data collected in the network, so as to understand the current resource utilization in an automated manner. In this paper, we take a first step towards cellular cognitive networks by proposing a framework that analyzes mobile operator data, builds profiles of the typical demand, and identifies unusual situations in network-wide usages. We evaluate our framework on two real-world mobile traffic datasets, and show how it extracts from these a limited number of meaningful mobile demand profiles. In addition, the proposed framework singles out a large number of outlying behaviors in both case studies, which are mapped to social events or technical issues in the network

BeppoSAX Observations of the Maser Sy2 Galaxy: ESO103-G35

We have made BeppoSAX observations of the Seyfert 2/1.9 galaxy ESO103-G35, which contains a nuclear maser source and is known to be heavily absorbed in the X-rays. Analysis of the X-ray spectra observed by SAX in October 1996 and 1997 yields a spectral index 0.74+/-0.07, typical of Seyfert galaxies and consistent with earlier observations of this source. The strong, soft X-ray absorption has column density 1.79E(23)/cm^2, again consistent with earlier results. The best fitting spectrum is that of a power law with a high energy cutoff at 29+/-10 keV, a cold, marginally resolved Fe Kalpha line with EW 290 eV (1996) and a mildly ionized Fe K-edge at 7.37 keV. The Kalpha line and cold absorption are consistent with origin in a accretion disk/torus through which our line-of-sight passes at a radial distance of $\sim 50$ pc. The Fe K-edge is mildly ionized suggesting the presence of ionized gas probably in the inner accretion disk, close to the central source or in a separate warm absorber. The data quality is too low to distinguish between these possibilities but the edge-on geometry implied by the water maser emission favors the former. Comparison with earlier observations of ESO103-G35 shows little/no change in spectral parameters while the flux changes by factors of a few on timescales of a few months. The 2--10 keV flux decreased by a factor of 2.7 between Oct 1996 and Oct 1997 with no detectable change in the count rate >20 keV suggesting a constant or delayed response reflection component. The high energy cutoff is lower than the typical 300keV values seen in Seyfert galaxies. A significant subset of similar sources would affect current models of the AGN contribution to the cosmic X-ray background which generally assume a high energy cutoff of 300 keV.Comment: 22 pages, postscript file, accepted for publication in Ap

A prompt diagnosis of late-onset congenital diaphragmatic hernia with Point of Care Ultrasound (POCUS) in a Pediatric Emergency Department.

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Fusing GPS Probe and Mobile Phone Data for Enhanced Land-Use Detection

International audienceProfiling the diversity of land use in modern cities by mining data related to human mobility represents a challenging problem in urban planning, transportation and smart city management. Previous work on mobile phone data (i.e., Call Detail Records) has shown the existence of strong correlations between the urban tissue and the associated mobile communication demand. Similarly, GPS traces of vehicles convey information on transportation demand and human activities that can be related to the land use of the neighborhood where they take place. In this paper, we investigate the land use patterns that emerge when studying simultaneously GPS traces of probe vehicles and mobile phone data collected by network providers. To this end, we extend previous definitions of mobile phone traffic signatures for land use detection, so as to incorporate additional information on human presence and mobility conveyed by GPS traces of vehicles. Leveraging these extended signatures, we exploit an unsupervised learning technique to identify classes of signatures that are distinctive of different land use. We apply our technique to real-world data collected in French and Italian cities. Results unveil the existence of signatures that are common to all studied areas and specific to particular land uses. The combined use of mobile phone data and GPS traces outperforms previous approaches when confronted to ground-truth information, and allows characterizing land use in greater detail than in the literature to date