As a 32 year old Ghanaian woman living in Luang Prabang and studying as an ophthalmologist, this gives me some food for thought!
LLMs can unmask pseudonymous users at scale with surprising accuracy
51–60 of 78 posts
Re: LLMs can unmask pseudonymous users at scale with surprising accuracy
#52I thought this would be more about stylometry but it's mostly about users literally posting the same identifiable information across multiple services, including in one example their age, dog name, profession. It's all classic dox profiling techniques. Even the things like spelling differences being regional signals and commonality to specific things being discussed. It's why one has to think about what is being post…
I’m curious if an LLM-based defense for this could be made. Like a browser plugin that warns you if you type identifiable information (like occupation) into a text field, and highlights turns of phrase that are “unusual” enough to be identifiable.
Re: LLMs can unmask pseudonymous users at scale with surprising accuracy
#53Re: LLMs can unmask pseudonymous users at scale with surprising accuracy
#54> If you request deletion of your Hacker News account, note that we reserve the right to refuse to (i) delete any of the submissions, favorites, or comments you posted on the Hacker News site Probably not GDPR-compliant then if comments can be deanonymised by LLMs.
This is probably the worst piece of policy on whole HN. It has a evil feel to it. If HN wasn't so interesting/valueable, this would be the single reason NOT to use it at all.
Why take away people's choice to use a forum with permanent comments? I know my comments will be here forever, but so will other people's comments. That's what makes HN valuable.
The alternative is what you see on reddit. A lot of threads from the past have posts deleted or overwritten with some script. You now have to dig through archive sites to find the comments, and you usually do find them.
I participate in Signal chats with self-destructing messages, too. But I post different things here and on Signal, under different usernames. Heck, after a few weeks I'll make another account here, anyway.
Even if you somehow deanonymize me, it's a risk I willingly took when I started posting.
Finally, if you go after HN for deleting comments, will you go after the many archive sites?
Re: LLMs can unmask pseudonymous users at scale with surprising accuracy
#55Anonymous account unmasking represents a new threat to anonymity. not just this technique with llms, but the earlier text similarity one. But I think it would be generally easier to counter in the same way. Use an llm or heuristics to pose as someone else. not only do you erase your traces, you add false positives in to the system which reduces the overall effectiveness of these techniques in the future. A bit of poi…
Like with browser fingerprinting, making it too unique is also an issue.
It may actually be a fine line. You may be flagged as an LLM later if your style is too generic and identified if your style is too unique.
Re: LLMs can unmask pseudonymous users at scale with surprising accuracy
#56There was a tool shared here that could show which accounts belong to the same person based on the writing patterns. Can't remember the name, but it found my old accounts on HN pretty accurately.
https://news.ycombinator.com/item?id=33755016
Way simpler than hnprofile from the sibling comment. This one used cosine similarity between user vocs - https://web.archive.org/web/20221126225241/https://stylometr...
Re: LLMs can unmask pseudonymous users at scale with surprising accuracy
#57Re: LLMs can unmask pseudonymous users at scale with surprising accuracy
#58Re: LLMs can unmask pseudonymous users at scale with surprising accuracy
#59> “This is a pretty new capability; previous approaches on re-identification generally required structured data, and two datasets with a similar schema that could be linked together.”
Right up there with Skynet, for me, has been the idea of disparate databases all being linked up by bad actors.
It appears as though DOGE illegally obtained taxpayer data from the IRS. I don’t trust DOGE to safeguard anything.
And the penalties do not seem to be very severe outside of HIPPA.
https://democracyforward.org/news/press-releases/new-details...