• refalo@programming.dev
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    1 day ago

    No it can’t. This story keeps getting posted all over the internet.

    Not only is it wrong, and not only do the researchers refuse to show their work (citing possible “misuse”), but it entirely depends on what kind of OPSEC failures the user happens to make.

    • Chozo@fedia.io
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      1 day ago

      If 90% of LinkedIn users are making the same OPSEC errors, then I’d say it works as advertised.

  • Ecco the dolphin@lemmy.ml
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    23 hours ago

    This headline sucks.

    They made a model of accounts that willingly linked their hackernews profiles to their linked-ins and made a model base on that (n= approx 990)

    They could “deanonymise” about 67% of those accounts from that n=990 candidate pool (alpha=.1) using their model (they already knew who they were, otherwise how could they verify a correct match?).

    When they threw in a bunch of accounts that had nothing to do with those first accounts (89k total accounts) accuracy dropped to around 55%-45% depending on choice of technique.

    1. first thing, those hn accts they trained on weren’t trying to be anonymous. They linked to their linked in profile. So, lie on the internet I guess

    2. this is just a starting point anyway, cheap and fast. That’s what to worry about. $1-$4 per account you’re trying to doxx like this.

    Just an interesting paper.

    • Onomatopoeia@lemmy.cafe
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      1 day ago

      Right?

      I have a linked in account which I haven’t touched in years, from a machine that no lonhers exists, on an internet connection I left behind.

      Good luck connectinge to that.

    • lasta@piefed.world
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      1 day ago

      Recall—that is, how many users were successfully deanonymized—was as high as 68 percent. Precision—meaning the rate of guesses that correctly identify the user—was up to 90 percent.

      I take that to mean there is a 90% match between anonymized posts and real life profiles for 68% of users and that it’s a minimum confidence level needed for a user to be considered deanonymized.