Thanks to Alejandro López for both links.
Researchers Use Ridesharing Cars to Sniff Out a Secret Spying Tool. Original link here. I'd like to see these wardriving experiments in my current (Montreal) or past (Madrid) towns.
Law enforcement's use of the surveillance devices known as stingrays, fake cell towers that can intercept communications and track phones, remains as murky as it is controversial, hidden in non-disclosure agreements and cloak-and-dagger secrecy. But a group of Seattle researchers has found a new method to track those trackers: by recruiting ridesharing vehicles as surveillance devices of their own.
For two months last year, researchers at the University of Washington paid drivers of an unidentified ridesharing service to keep custom-made sensors in the trunks of their cars, converting those vehicles into mobile cellular data collectors. They used the results to map out practically every cell tower in the cities of Seattle and Milwaukee—along with at least two anomalous transmitters they believe were likely stingrays, located at the Seattle office of the US Customs and Immigration Service, and the Seattle-Tacoma Airport.
Lawyers, academics, and activists are now questioning that reasoning. Judicial processes are, by and large, open to the public. Judges must give reasons for their most important actions, such as sentencing. When an algorithmic scoring process is kept secret, it is impossible to challenge key aspects of it. How is the algorithm weighting different data points, and why? Each of these inquiries is crucial to two core legal principles: due process, and the ability to meaningfully appeal an adverse decision.
[...] So Bickel and co got to work first by text mining most of the Facebook status updates and then data mining most of the likes data set. Any patterns they found, they then tested by looking for people with similar patterns in the remaining data and seeing if they also had the same level of substance use.
Data Links is a periodic blog post published on Sundays (specific time may vary) which contains interesting links about data science, machine learning and related topics. You can subscribe to it using the general blog RSS feed or this one, which only contains these articles, if you are not interested in other things I might publish.
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