Resultados 1 a 3 de 3
  1. #1
    WHT-BR Top Member
    Data de Ingresso
    Dec 2010

    GPS revela identidade de usuário de celular

    Pesquisa descobriu que dados enviados pelo GPS criam uma uma espécie de impressão digital capaz de diferenciar um celular de outro.

    Informações anônimas sobre a localização geradas por um smartphone não são tão anônimas assim. Um estudo feito por pesquisadores do MIT e da Universidade Católica de Louvain, na Bélgica, descobriu que é possível saber a identidade de um usuário a partir do monitoramento do GPS do celular.

    Segundo os cientistas, esses dados de localização produzem uma espécie de impressão digital, capaz de diferenciar um celular do outro a partir dos vestígios dos dados enviados para a torre da operadora, quando faz ou recebe uma ligação ou um sms.

    Para chegar a essa conclusão, os pesquisadores analisaram 15 meses de informações anônimas emitidas pelo celular de 1,5 milhão de usuários na Europa entre 2006 e 2007. Baseados em atualizações de hora em hora da localização emitida pelos celular, os pesquisadores seguiram os rastros desses aparelhos até as torres mais próximas conforme eles enviavam ou recebiam ligações e mensagens SMS.

    Com apenas quatro ligações ou SMS, os pesquisadores conseguiram identificar 95% dos usuários, com duas, eles conseguiam identificar 50%. “Nós mostramos que os traços móveis de um pessoa são únicos, enfatizando a importância dos movimentos em prol da privacidade individual”, afirmaram os pesquisadores no estudo.

    Os resultados da pesquisa são importantes uma vez que a Apple e o Google, por exemplo, já admitiram coletar e guardar informações de localização dos usuários. Os pedidos por parte de governos por esse tipo de informação também aumentaram.

    No Brasil, as operadoras de telefonia querem usar esses dados para fins comerciais. O assunto é tema de debate do Marco Civil da Internet, que está sendo discutido na Câmara dos Deputados.
    GPS revela identidade de usu

  2. #2
    WHT-BR Top Member
    Data de Ingresso
    Dec 2010

    How hard is it to 'de-anonymize' cellphone data?

    The proliferation of sensor-studded cellphones could lead to a wealth of data with socially useful applications — in urban planning, epidemiology, operations research and emergency preparedness, among other things. Of course, before being released to researchers, the data would have to be stripped of identifying information. But how hard could it be to protect the identity of one unnamed cellphone user in a data set of hundreds of thousands or even millions?

    According to a paper appearing this week in Scientific Reports, harder than you might think. Researchers at MIT and the Université Catholique de Louvain, in Belgium, analyzed data on 1.5 million cellphone users in a small European country over a span of 15 months and found that just four points of reference, with fairly low spatial and temporal resolution, was enough to uniquely identify 95 percent of them.

    In other words, to extract the complete location information for a single person from an “anonymized” data set of more than a million people, all you would need to do is place him or her within a couple of hundred yards of a cellphone transmitter, sometime over the course of an hour, four times in one year. A few Twitter posts would probably provide all the information you needed, if they contained specific information about the person’s whereabouts.

    The first author on the paper is Yves-Alexandre de Montjoye, a graduate student in the research group of Toshiba Professor of Media Arts and Science Sandy Pentland. He’s joined by César Hidalgo, an assistant professor of media arts and science; Vincent Blondel, a visiting professor at MIT and a professor of applied mathematics at Université Catholique; and Michel Verleysen, a professor of electrical engineering at Université Catholique.

    Focusing the debate

    Hidalgo’s group specializes in applying the tools of statistical physics to a wide range of subjects, from communications networks to genetics to economics. In this case, he and de Montjoye were able to use those tools to uncover a simple mathematical relationship between the resolution of spatiotemporal data and the likelihood of identifying a member of a data set.

    According to their formula, the probability of identifying someone goes down if the resolution of the measurements decreases, but less than you might think. Reporting the time of each measurement as imprecisely as sometime within a 15-hour span, or location as imprecisely as somewhere amid 15 adjacent cell towers, would still enable the unique identification of half the people in the sample data set.

    But while its initial application may be discouraging, de Montjoye and Hidalgo hope that their formula will provide a way for researchers and policy analysts to reason more rigorously about the privacy safeguards that need to be put in place when they’re working with aggregated location data.

    “Both César and I deeply believe that we all have a lot to gain from this data being used,” de Montjoye says. “This formula is something that could be useful to help the debate and decide, OK, how do we balance things out, and how do we make it a fair deal for everyone to use this data?”

    Everybody’s different

    In the data set that the researchers analyzed, the location of a cellphone was inferred solely from that of the cell tower it was connected to, and the time of the connection was given as falling within a one-hour interval. Each cellphone had a unique, randomly generated identifying number, so that its movement could be traced over time. But there was no information connecting that number to the phone’s owner.

    The researchers randomly selected a representative sampling from the set of 1.5 million cellphone traces and, for each trace, began choosing points at random. For 95 percent of the traces, just four randomly selected points was enough to distinguish them from all other traces in the database. In the worst (or, from another perspective, best) case, 11 measurements were necessary.

    “There’s a concern with this data, to what extent can we preserve anonymity,” says Luis Bettencourt, a professor at the Santa Fe Institute who studies social systems. “What they are showing here, quite clearly, is that it’s very hard to preserve anonymity.”

    But for Bettencourt, the uniqueness of people’s trajectories through cities is itself precisely the type of information that analysis of cellphone data is meant to uncover. “This is interesting, from a scientific point of view, to understand how people use urban space,” Bettencourt says. “It shows what kind of social systems cities are.”

    The researchers suspect that similar relationships might hold for other types of data. “I would not be surprised if a similar result — maybe requiring more points — would, for example, extend to web browsing,” Hidalgo says. “The space of potential combinations is really large. When a person is, in some sense, being expressed in a space in which the total number of combinations is huge, the probability that two people would have the same exact trajectory — whether it’s walking or browsing — is almost nil.”
    How hard is it to 'de-anonymize' cellphone data? - MIT News Office

  3. #3
    WHT-BR Top Member
    Data de Ingresso
    Dec 2010
    O GPS ficou por conta da criatividade da imprensa brasileira.

    In the data set that the researchers analyzed, the location of a cellphone was inferred solely from that of the cell tower it was connected to

    Artigo original:

    We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.
    Our dataset contains 15 months of mobility data for 1.5 M people, a significant and representative part of the population of a small European country, and roughly the same number of users as the location-based service Foursquare®31. Just as with smartphone applications or electronic payments, the mobile phone operator records the interactions of the user with his phone. This creates a comparable longitudinally sparse and discrete database [Fig. 3]. On average, 114 interactions per user per month for the nearly 6500 antennas are recorded. Antennas in our database are distributed throughout the country and serve, on average, ~ 2000 inhabitants each, covering areas ranging from 0.15 km2 in cities to 15 km2 in rural areas. The number of antennas is strongly correlated with population density (R2 = .6426) [Fig. 3C]. The same is expected from businesses, places in location-based social networks, or WiFi hotspots.

    Trace of an anonymized mobile phone user during a day.The dots represent the times and locations where the user made or received a call. Every time the user has such an interaction, the closest antenna that routes the call is recorded.
    Última edição por 5ms; 30-03-2013 às 13:45.

Permissões de Postagem

  • Você não pode iniciar novos tópicos
  • Você não pode enviar respostas
  • Você não pode enviar anexos
  • Você não pode editar suas mensagens