3 ms·
I see there is interest in this observation so I used a little ruby (.to_s(2)[-22..-17].to_i(2)) to get the datacenter id. Then ran it on a few Twitter account
by aboutruby 8y ago
I see there is interest in this observation so I used a little ruby (.to_s(2)[-22..-17].to_i(2)) to get the datacenter id.
Then ran it on a few Twitter accounts: https://pastebin.com/w8Dnj5kM https://pastebin.com/w8Dnj5kM
It does work, and it's going to be hard to patch
edit: I realized you don't only get one location but the whole location history of a Twitter user. Also locating Twitter's data centers as it doesn't seem to be public information
- stuck_in_matrix 8y agoThis is really interesting. When I did the original analysis on datacenter / server ids, I didn't think about correlation with user accounts. Nice observation!
- Sommer 8y agoShould be pretty simple to check the tweet geo or location mentions per server id to see of they imply a correlation with geographic area of the server. Then you're just one hop to knowing where (or where not) other tweeters are.
- aboutruby 8y agoI was curious and just did that :) https://gist.github.com/localhostdotdev/48ed13972c3e5391a47f8e3dd7b9e0dd https://gist.github.com/localhostdotdev/48ed13972c3e5391a47f... (small sample of ~1500 localized tweets)
- jaytaylor 8y agoThey could "fix" it by periodically rotating the DC and server IDs. I wish it was not so easily fixable, because this will break the key space reduction trick ;), but unfortunately such a solution is feasible and would come with the side-effect of drastically increasing the required scanning space.
- atian 8y agoWhat are you guys even smoking. The ID segment is 5 bits long. You have an extra server ID bit making the datacenter results more significant than they are.