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

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

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

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

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

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

A Tale of Ten Cities: Characterizing Signatures of Mobile Traffic in Urban Areas

International audienceUrban landscapes present a variety of socio-topological environments that are associated to diverse human activities. As the latter affect the way individuals connect with each other, a bound exists between the urban tissue and the mobile communication demand. In this paper, we investigate the heterogeneous patterns emerging in the mobile communication activity recorded within metropolitan regions. To that end, we introduce an original technique to identify classes of mobile traffic signatures that are distinctive of different urban fabrics. Our proposed technique outperforms previous approaches when confronted to ground-truth information, and allows characterizing the mobile demand in greater detail than that attained in the literature to date. We apply our technique to extensive real-world data collected by major mobile operators in ten cities. Results unveil the diversity of baseline communication activities across countries, but also evidence the existence of a number of mobile traffic signatures that are common to all studied areas and specific to particular land uses

Nanofat 2.0: experimental evidence for a fat grafting rich in mesenchymal stem cells.

Different strategies have been developed in the last decade to obtain fat grafts as rich as possible of mesenchymal stem cells, so exploiting their regenerative potential. Recently, a new kind of fat grafting, called "nanofat", has been obtained after several steps of fat emulsification and filtration. The final liquid suspension, virtually devoid of mature adipocytes, would improve tissue repair because of the presence of adipose mesenchymal stem cells (ASCs). However, since it is probable that many ASCs may be lost in the numerous phases of this procedure, we describe here a novel version of fat grafting, which we call "nanofat 2.0", likely richer in ASCs, obtained avoiding the final phases of the nanofat protocol. The viability, the density and proliferation rate of ASCs in nanofat 2.0 sample were compared with samples of nanofat and simple lipoaspirate. Although the density of ASCs was initially higher in lipoaspirate sample, the higher proliferation rate of cells in nanofat 2.0 virtually filled the gap within 8 days. By contrast, the density of ASCs in nanofat sample was the poorest at any time. Results show that nanofat 2.0 emulsion is considerably rich in stem cells, featuring a marked proliferation capability

TRANSIT: Fine-Grained Human Mobility Trajectory Inference at Scale with Mobile Network Signaling Data

International audienceCall detail records (CDR) collected by mobile phone network providers have been largely used to model and analyze human-centric mobility. Despite their potential, they are limited in terms of both spatial and temporal accuracy thus being unable to capture detailed human mobility information. Network Signaling Data (NSD) represent a much richer source of spatio-temporal information currently collected by network providers, but mostly unexploited for fine-grained reconstruction of human-centric trajectories. In this paper, we present TRANSIT, TRAjectory inference from Network SIgnaling daTa, a novel framework capable of proceessing NSD to accurately distinguish mobility phases from stationary activities for individual mobile devices, and reconstruct, at scale, fine-grained human mobility trajectories, by exploiting the inherent recurrence of human mobility and the higher sampling rate of NSD. The validation on a ground-truth dataset of GPS trajectories showcases the superior performance of TRANSIT (80% precision and 96% recall) with respect to state-of-the-art solutions in the identification of movement periods, as well as an average 190 m spatial accuracy in the estimation of the trajectories. We also leverage TRANSIT to process a unique large-scale NSD dataset of more than 10 millions of individuals and perform an exploratory analysis of city-wide transport mode shares, recurrent commuting paths, urban attractivity and analysis of mobility flows

Dupilumab in the treatment of severe uncontrolled chronic rhinosinusitis with nasal polyps (CRSwNP): A multicentric observational Phase IV real-life study (DUPIREAL)

Background Chronic rhinosinusitis with nasal polyps (CRSwNP) is associated with significant morbidity and reduced health-related quality of life. Findings from clinical trials have demonstrated the effectiveness of dupilumab in CRSwNP, although real-world evidence is still limited. Methods This Phase IV real-life, observational, multicenter study assessed the effectiveness and safety of dupilumab in patients with severe uncontrolled CRSwNP (n = 648) over the first year of treatment. We collected data at baseline and after 1, 3, 6, 9, and 12 months of follow-up. We focused on nasal polyps score (NPS), symptoms, and olfactory function. We stratified outcomes by comorbidities, previous surgery, and adherence to intranasal corticosteroids, and examined the success rates based on current guidelines, as well as potential predictors of response at each timepoint. Results We observed a significant decrease in NPS from a median value of 6 (IQR 5–6) at baseline to 1.0 (IQR 0.0–2.0) at 12 months (p < .001), and a significant decrease in Sino-Nasal Outcomes Test-22 (SNOT-22) from a median score of 58 (IQR 49–70) at baseline to 11 (IQR 6–21; p < .001) at 12 months. Sniffin' Sticks scores showed a significant increase over 12 months (p < .001) compared to baseline. The results were unaffected by concomitant diseases, number of previous surgeries, and adherence to topical steroids, except for minor differences in rapidity of action. An excellent-moderate response was observed in 96.9% of patients at 12 months based on EPOS 2020 criteria. Conclusions Our findings from this large-scale real-life study support the effectiveness of dupilumab as an add-on therapy in patients with severe uncontrolled CRSwNP in reducing polyp size and improving the quality of life, severity of symptoms, nasal congestion, and smell