%0 Journal Article %D 2021 %T Give more data, awareness and control to individual citizens, and they will help COVID-19 containment %A Mirco Nanni %A Andrienko, Gennady %A Barabasi, Albert-Laszlo %A Boldrini, Chiara %A Bonchi, Francesco %A Cattuto, Ciro %A Chiaromonte, Francesca %A Comandé, Giovanni %A Conti, Marco %A Coté, Mark %A Dignum, Frank %A Dignum, Virginia %A Domingo-Ferrer, Josep %A Ferragina, Paolo %A Fosca Giannotti %A Riccardo Guidotti %A Helbing, Dirk %A Kaski, Kimmo %A Kertész, János %A Lehmann, Sune %A Lepri, Bruno %A Lukowicz, Paul %A Matwin, Stan %A Jiménez, David Megías %A Anna Monreale %A Morik, Katharina %A Oliver, Nuria %A Passarella, Andrea %A Passerini, Andrea %A Dino Pedreschi %A Pentland, Alex %A Pianesi, Fabio %A Francesca Pratesi %A S Rinzivillo %A Salvatore Ruggieri %A Siebes, Arno %A Torra, Vicenc %A Roberto Trasarti %A Hoven, Jeroen van den %A Vespignani, Alessandro %X The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. %8 2021/02/02 %@ 1572-8439 %G eng %U https://link.springer.com/article/10.1007/s10676-020-09572-w %! Ethics and Information Technology %R https://doi.org/10.1007/s10676-020-09572-w %0 Journal Article %J International Journal of Data Science and Analytics %D 2020 %T Human migration: the big data perspective %A Alina Sirbu %A Andrienko, Gennady %A Andrienko, Natalia %A Boldrini, Chiara %A Conti, Marco %A Fosca Giannotti %A Riccardo Guidotti %A Bertoli, Simone %A Jisu Kim %A Muntean, Cristina Ioana %A Luca Pappalardo %A Passarella, Andrea %A Dino Pedreschi %A Pollacci, Laura %A Francesca Pratesi %A Sharma, Rajesh %X How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants. %B International Journal of Data Science and Analytics %P 1–20 %8 2020/03/23 %@ 2364-4168 %G eng %U https://link.springer.com/article/10.1007%2Fs41060-020-00213-5 %! International Journal of Data Science and Analytics %R https://doi.org/10.1007/s41060-020-00213-5