Charlie writes, “There is a smoke shop in Scotia NY, owned by a young black man. There are many, many smoke shops in the capital region, but the rest are owned by white people.
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Head-shop CCTV catches police informant/undercover planting crack
A woman who valet-parked her car at Rochester airport returned to find a notice informing her that the valet had searched her car, on orders from the TSA.
CNet’s Declan McCullagh reports on a rumor that Google is testing a system for encrypting its users’ files on Google Drive; they are reportedly considering the move as a means of making it harder for government spies to harvest user-data.
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Google experimenting with spy-resistant encrypted Google Drive
Windell at Evil Mad Scientist Labs has dredged up an amazing project book from the Internet Archive: How to Build a Working Digital Computer (1967) (by Edward Alcosser, James P. Phillips, and Allen M. Wolk) contains a full set of instructions for building a working computer out of paperclips and various bits and bobs from the local hardware store. You can even use paperclips for switches (though, as Windell notes, “Arrays of paperclip logic gates can get pretty big, pretty fast.”) The instructions include a read-only drum memory for storing the computer program (much like a player piano roll), made from a juice can, with read heads made from bent paper clips. A separate manually-operated “core” memory (made of paper-clip switches) is used for storing data. So can this “paper clip” computer actually built, and if so, would it work? Apparently yes, on both counts. Cleveland youngsters Mark Rosenstein and Kenny Antonelli built one named “ Emmerack ” in 1972 (albeit substituting Radio Shack slide switches for most of the paper clips), and another was built in 1975 by the Wickenburg High School Math Club in Arizona. And, at least one modern build has been completed, as you can see on YouTube . How to Build a Working Digital Computer… out of paperclips ( via O’Reilly Radar )
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HOWTO build a working digital computer out of paperclips (and stuff)
Daniel Ryan describes his music as “a mix of Japanese folk music and glitch hop.” This isn’t normally my sort of thing — I pretty much only listen to music with words — but I played this one three times in a row this morning. There’s a lot of clever stuff going on here that I lack the vocabulary to describe but possess the aesthetic apparatus to appreciate. According to one redditor, the folk song is this track off the Samurai Champloo soundtrack. Nagasaki
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Japanese folk music glitch hop
Greg Costikyan sez, inBloom , a Gates-funded non-profit to harness data to improve grade school education, has partnered with New York and eight other states to encourage the development of apps to “further education” by using intimate data about students, without parental consent and with no ability for parents to opt out. Among the data shared are name, address, phone numbers, test scores, grades, economic status, test scores, disciplinary records, picture, email, race, developmental delay… just about everything conceivable , and all specific, none of it anonymized. inBloom has arrangements with nine states (New York, Massachusetts, Louisiana, Colorado, Illinois, North Carolina, Georgia, Delaware and Kentucky) to do this. The XML schema used are downloadable here . Anyone can register as a developer and start using “sample” data, but “real” data is supposedly only available to developers with contracts with a school board. But this includes for-profit, third party developers, such as, say, Amplify, a News Corp subsidiary with a contract with New York. And it doesn’t appear there are any constraints on their use of this data. Who is Stockpiling and Sharing Private Information About New York Students? ( Thanks, Greg! )
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American public schools in 9 states sharing every conceivable personal detail of their students with third parties
ATM skimming isn’t limited to ATMs! There are lots of terminals that ask you to swipe your card and/or enter a PIN, and many of them are less well-armored and -policed than actual cashpoints. Skimmers have been found on train-ticket machines, parking meters and other payment terminals. Once a crook has got your card number and sign-on data, they can use that to raid a your account at an ATM. Brian Krebs has a look at some of these devices, including a full-on fascia for a cheapie ATM discovered in latinamerica. The organization also is tracking a skimming trend reported by three countries (mainly in Latin America) in which thieves are fabricating fake ATM fascias and placing them over genuine ATMs, like the one pictured below. After entering their PIN, cardholders see an ‘out-of-order’ message. EAST said the fake fascias include working screens so that this type of message can be displayed. The card details are compromised by a skimming device hidden inside the fake fascia, and the PINs are captured via the built-in keypad, which overlays the real keypad underneath. This reminds me a little of the evolution of payphones — the armadillos of the device world! — and the look-alike COCOTS (customer-owned coin-operated telephones) that presented very soft targets if you could scry through their camouflage. Cash Claws, Fake Fascias & Tampered Tickets
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ATM skimming comes to non-ATM payment terminals in train stations, etc
Unique in the Crowd: The privacy bounds of human mobility , a Nature Scientific Reports paper by MIT researchers and colleagues at Belgium’s Universite Catholique de Louvain, documents that 95% of “anonymous” location data from cellphone towers can be de-anonymized to the individual level. That is, given data from a region’s cellular towers, the researchers can ascribe individuals to 95% of the data-points. “We show that the uniqueness of human mobility traces is high, thereby emphasizing the importance of the idiosyncrasy of human movements for individual privacy,” they explain. “Indeed, this uniqueness means that little outside information is needed to re-identify the trace of a targeted individual even in a sparse, large-scale, and coarse mobility dataset. Given the amount of information that can be inferred from mobility data, as well as the potentially large number of simply anonymized mobility datasets available, this is a growing concern.” The data they studied involved users in an unidentified European country, possibly Belgium, and involved anonymized data collected by their carriers between 2006 and 2007. Anonymized Phone Location Data Not So Anonymous, Researchers Find [Wired/Kim Zetter]
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Researchers show method for de-anonymizing 95% of “anonymous” cellular location data