On the privacy-conscientious use of mobile phone data

Montjoye, Yves-Alexandre de; Gambs, Sébastien; Blondel, Vincent D.; Canright, Geoffrey; Cordes, Nicolas de; Deletaille, Sébastien; Engø‐Monsen, Kenth; García-Herranz, Manuel; Kendall, Jake; Kerry, Cameron; Krings, Gautier; Letouzé, Emmanuel; Luengo-Oroz, Miguel; Oliver, Núria; Rocher, Luc; Rutherford, Alex; Smoreda, Zbigniew; Steele, James; Wetter, Erik; Pentland, Alex; Bengtsson, Linus

doi:10.1038/sdata.2018.286

Cited by 143 publications

(113 citation statements)

References 16 publications

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“…To make outputs from CDRs more accessible, the initiatives like the Open Algorithms project and the FlowKit, a CDR analytics toolset developed by the Flowminder Foundation and the WorldPop research group at the University of Southampton, aim to unlock the potential of private data for public good in a privacy-conscientious, scalable, socially and economically sustainable manner. 83,84 Moreover, it is necessary to create adequate legislative and regulatory frameworks to safeguard confidentiality of the information and ensure the ethical use of data for development projects. 81 Second, as mobile phone or social media users only represent a proportion of the whole population, the interpretation of mobility estimates must account for biases introduced by heterogeneous use of mobile phones, social media platforms and the internet.…”

Section: Discussionmentioning

confidence: 99%

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

Lai

Farnham

Ruktanonchai

et al. 2019

Journal of Travel Medicine

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Rationale for review:The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data. Key findings:Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel. Conclusions:Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin-destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.

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Section: Discussionmentioning

confidence: 99%

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

Lai

Farnham

Ruktanonchai

et al. 2019

Journal of Travel Medicine

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“…New data sources at our disposal include not only the passively observed big data streams from mobile phones but also detailed environmental data and local sensor information from distributed devices, internet search information, pathogen genomic data that can be generated rapidly during an outbreak to inform the response, 7 and crowd-sourced approaches to monitoring rapidly evolving emergencies. 8 Data sharing platforms and standardised aggregation approaches that protect the privacy of personal data are being developed, 9 and increasing internet connectivity allows for rapid data transfer and communication between geographically disparate teams of responders. Methodologically, powerful ensemble modelling approaches are being developed that combine multiple forecasts to minimise uncertainty.…”

Section: Improving Epidemic Surveillance and Response: Big Data Is Dementioning

confidence: 99%

Improving epidemic surveillance and response: big data is dead, long live big data

Buckee

2020

The Lancet Digital Health

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“…Efforts directed toward providing access to telecom large-scale human behavioral data in a privacy-preserving manner [41] are necessary. Real-time settings raise critical issues concerning computational infrastructure, big data frameworks and analytics.…”

Section: Summary and Visionmentioning

confidence: 99%

Big Data Processing, Analysis and Applications in Mobile Cellular Networks

Brdar

Novovic

Grujić

et al. 2019

Lecture Notes in Computer Science

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When coupled with spatio-temporal context, location-based data collected in mobile cellular networks provide insights into patterns of human activity, interactions, and mobility. Whilst uncovered patterns have immense potential for improving services of telecom providers as well as for external applications related to social wellbeing, its inherent massive volume make such 'Big Data' sets complex to process. A significant number of studies involving such mobile phone data have been presented, but there still remain numerous open challenges to reach technology readiness. They include efficient access in privacy-preserving manner, high performance computing environments, scalable data analytics, innovative data fusion with other sources-all finally linked into the applications ready for operational mode. In this chapter, we provide a broad overview of the entire workflow from raw data access to the final applications and point out the critical challenges in each step that need to be addressed to unlock the value of data generated by mobile cellular networks.

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On the privacy-conscientious use of mobile phone data

Cited by 143 publications

References 16 publications

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

Improving epidemic surveillance and response: big data is dead, long live big data

Big Data Processing, Analysis and Applications in Mobile Cellular Networks

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