Facebook Knows How to Track You Using the Dust on Your Camera Lens
Originally shared by Edward Morbius
Facebook Knows How to Track You Using the Dust on Your Camera Lens
...In 2014, Facebook filed a patent application for a technique that employs smartphone data to figure out if two people might know each other. The author, an engineering manager at Facebook named Ben Chen, wrote that it was not merely possible to detect that two smartphones were in the same place at the same time, but that by comparing the accelerometer and gyroscope readings of each phone, the data could identify when people were facing each other or walking together. That way, Facebook could suggest you friend the person you were talking to at a bar last night, and not all the other people there that you chose not to talk to....
Not just dust.
Not just Facebook.
#33bits
A critical point about social media -- or any public or trackable posting of data, is that it leaves identifiers which can be traced back.
I've long been aware of persistent identifiers -- the pattern of yellow dots that colour laser printers leave, as an example (Whistleblower Reality Winner was caught based on this, due to copies of documents posted online by The Intercept), or the patterns of dead pixels in most digital cameras. But even similar patterns of dust on lenses -- an ephemeral identifier -- can be used to match up devices. As can location and timing data, gait data, and more, available from the gyroscopes which let you play pinball or tilt-ball games on your smartphone or tablet.
Or facial recognition of faces in crowds. A Hacker News commenter notes that he and his current partner turned out to have both been in a photo taken at a march before they met, which was auto-tagged after they'd followed one another online.
With 7.3 billion people in the world, all it takes are 33 bits of distinct identifying information. That can come from all kinds of sources, but location, purchase data, facial recognition, device "fingerprints" (ranging from specifically-encoded UUIDs to incidental patterns such as described here) are often sufficient. And centralised systems create repositories from which a tremendous number of such patterns can be sorted, sifted, and matched automatically.
I'm not sure how future options, including distributed and decentralised systems, will change this. But it's something I'm very much keeping in mind.
https://gizmodo.com/facebook-knows-how-to-track-you-using-the-dust-on-your-1821030620
https://gizmodo.com/facebook-knows-how-to-track-you-using-the-dust-on-your-1821030620
Facebook Knows How to Track You Using the Dust on Your Camera Lens
...In 2014, Facebook filed a patent application for a technique that employs smartphone data to figure out if two people might know each other. The author, an engineering manager at Facebook named Ben Chen, wrote that it was not merely possible to detect that two smartphones were in the same place at the same time, but that by comparing the accelerometer and gyroscope readings of each phone, the data could identify when people were facing each other or walking together. That way, Facebook could suggest you friend the person you were talking to at a bar last night, and not all the other people there that you chose not to talk to....
Not just dust.
Not just Facebook.
#33bits
A critical point about social media -- or any public or trackable posting of data, is that it leaves identifiers which can be traced back.
I've long been aware of persistent identifiers -- the pattern of yellow dots that colour laser printers leave, as an example (Whistleblower Reality Winner was caught based on this, due to copies of documents posted online by The Intercept), or the patterns of dead pixels in most digital cameras. But even similar patterns of dust on lenses -- an ephemeral identifier -- can be used to match up devices. As can location and timing data, gait data, and more, available from the gyroscopes which let you play pinball or tilt-ball games on your smartphone or tablet.
Or facial recognition of faces in crowds. A Hacker News commenter notes that he and his current partner turned out to have both been in a photo taken at a march before they met, which was auto-tagged after they'd followed one another online.
With 7.3 billion people in the world, all it takes are 33 bits of distinct identifying information. That can come from all kinds of sources, but location, purchase data, facial recognition, device "fingerprints" (ranging from specifically-encoded UUIDs to incidental patterns such as described here) are often sufficient. And centralised systems create repositories from which a tremendous number of such patterns can be sorted, sifted, and matched automatically.
I'm not sure how future options, including distributed and decentralised systems, will change this. But it's something I'm very much keeping in mind.
https://gizmodo.com/facebook-knows-how-to-track-you-using-the-dust-on-your-1821030620
https://gizmodo.com/facebook-knows-how-to-track-you-using-the-dust-on-your-1821030620
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