Why just blocking Meta's Threads won't be enough to protect your privacy once they join the fediverse - eviltoast
  • ArbiterXero@lemmy.world
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    1 year ago

    There’s absolutely new threat vectors.

    Let’s get specific, since you claim to work in analytics.

    If I can link your lemmy account to a Facebook account, then I can uniquely identify you.

    Any posted links from the meta federation that open in a browser can use standard fingerprinting to identify you. That still exists today, but given I served your ip the lemmy-article and you then clicked the link in it, I can now join the two by ip alone. Now there could be multiple people browsing at your house, so this will have to be a time series and probability, but the correlation will eventually be strong enough to say with reasonable certainty. This works especially well if I put something like “google amp” or a url shortened in the middle of the links, because then I don’t need to have my advertising/tracking code on the website. Without the federation I can’t link it to an account and I can’t see your browsing history on pages that my “analytics code” isn’t on.

    There’s your netsec threat vector.

    From the social perspective, the threat vector is exactly the same as Cambridge analytica. I notice that you as a unique user fit pattern x and I start tailoring the links you see and don’t see based on what I want to change about you. Now it’s not AS effective because the real effectiveness there was removing articles that disprove some of my bullshit. Because I’m just a node in the federation, I can’t prevent other nodes from showing you conflicting info.

    Selling manipulation is a social threat vector, but if you want netsec, you now have both.

    EEE becomes important because it increases effectiveness and value of the manipulation that I sell.

    Then suddenly you wake up, everyone has voted for brexit or some orange scammer against their own self interest. If you work with big data, then you know that you can change a lot of individual points in small nearly imperceptible ways (to that specific data) that can make huge changes to the dataset as a whole.