privacy   208780

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Sorry, your data can still be identified even if it’s anonymized
Carlo Ratti, the MIT Senseable City Lab founder who co-authored the study in IEEE Transactions on Big Data, says that the research process made them feel “a bit like ‘white hat’ or ‘ethical’ hackers” in a news release. First, they combined two anonymized datasets of people in Singapore, one of mobile phone logs and the other of transit trips, each containing “location stamps” detailing just the time and place of each data point. Then they used an algorithm to match users whose data overlapped closely between each set–in other words, they had phone logs and transit logs with similar time and location stamps–and tracked how closely those stamps matched up over time, eliminating false positives as they went. In the end, it took a week to match up 17% of the users and 11 weeks to get to a 95% rate of accuracy. (With the added GPS data from smartphones, it took less than a week to hit that number.)

While the MIT group wasn’t trying to unmask specific users in this dataset, they proved that someone acting in bad faith could merge such anonymized datasets with personal ones using the same process, easily pinning the timestamps together to figure out who was who.
privacy  telecommunications  infosec 
yesterday by campylobacter
Should We Be Worried About Computerized Facial Recognition? | The New Yorker
The technology could revolutionize policing, medicine, even agriculture—but its applications can easily be weaponized, David Owen writes.
surveillance  privacy  data  dystopia  technology 
yesterday by basemaly

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