As the murder of the opposition politician Boris Nemtsov last week reminds us, the political situation in Russia is not just difficult, but extremely dangerous. Presumably hoping that technology might offer a relative safe way to cope with this situation, a Russian NGO has announced that it will be launching a nationwide social network dedicated to fighting bribery and corruption. You might expect that anonymity would be a crucial aspect, given the risks faced by those who choose to join. And yet, as this RT article explains, that’s not the case (via @prfnv):
the new project will have one major difference from existing social networks — a complete lack of anonymity. Membership will only be granted by invitation from existing members, and even when this condition is met, the institute that launches the project promises to open accounts only after verifying the identity of potential members in real life.
The users will have to provide a lot of details about themselves — from name and date of birth, to place of work, e-mail and phone numbers. The people launching the project say that this is a necessary measure to prevent attempted slander, which they see as the main danger threatening their network.
Tag: Anonymity
Yet Another Report Showing ‘Anonymous’ Data Not At All Anonymous
As companies expand the amount of data hoovered up via their subscribers, a common refrain to try and ease public worry is that consumers shouldn’t worry because this data is “anonymized.” However, time and time again studies have highlighted how it’s not particularly difficult to tie these data sets to consumer identities — usually with only the use of a few additional contextual clues. It doesn’t really matter whether we’re talking about cellular location data, GPS data, taxi data or NSA metadata, the basic fact is these anonymous data sets aren’t really anonymous.
The latest in a long stream of such studies comes from MIT, where researchers explored (the actual study is paywalled) whether they could glean unique identities from “anonymous” user data using a handful of contextual clues. Studying the purportedly anonymous credit card transactions of 1.1 million users at 10,000 retail locations over a period of three months, the researchers found they could identify 90% of the users’ names by using four additional data points like the dates and locations of four purchases. Using three clues, including more specific points like the exact price of a purchase, allowed the identifying of 94% of the consumers. Intentionally trying to make the data points less precise didn’t help protect consumer privacy much