Large-Scale Online Deanonymization with LLMs

I post under my real name here, pretty much the only place I post. It keeps me honest and straight in what I say when I choose to say it. I tried talking to my children about leaving as clean of a footprint on the internet as one can in anticipation of future people/systems taking that into consideration. I don't know what it will be but I would expect some adversarial stuff. Trying to keep clean is what I'd prefer for myself and my kids.

On other hand, the Neal Stephenson's Fall or, Dodge in Hell book has an interesting idea in early phase of the book where a person agrees to what we now know "flood the zone with sh*t" (Steve Bannon's sadly very effective strategy) to battle some trolls. Instead of trying to keep clean, the intent is just to spam like crazy with anything so nobody understands the core. It is cleverly explored in the book albeit for too short of a time before moving into the virtual reality. I think there are a few people out here right now practicing this.

many people tend to overlook how little information is needed for successful de-anonymization.

i like to introduce students to de-anonymization with an old paper "Robust De-anonymization of Large Sparse Datasets" published in the ancient history of 2008 (https://www.cs.cornell.edu/~shmat/shmat_oak08netflix.pdf):

"We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix [...]. We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber’s record in the dataset."

and that was 20 years ago! de-anonymization techniques have improved by leaps and bounds since then, alongside the massive growth in various technology that enhances/enables various techniques.

i think the age of (pseduo-)anonymous internet browsing will be over soon. certainly within my lifetime (and im not that young!). it might be by regulation, it might be by nature of dragnet surveillance + de-anonymization, or a combination of both. but i think it will be a chilling time.

A related past submission comes to mind:

Show HN: Using stylometry to find HN users with alternate accounts

https://news.ycombinator.com/item?id=33755016 - Nov 2022, 519 comments

I'm not sure the practical implications are as dramatic as the paper suggests. Most adversaries who would want to deanonymize people at scale (governments, corporations) already have access to far more direct methods. The people most at risk from this are probably activists and whistleblowers in jurisdictions where those direct methods aren't available, not average users.

As people will point out, the OSINT techniques described are nothing new - typically, in the past, you could de-anonymize based on writing style or niche topics/interests. Totally deanonymization can occur if any of these accounts link to profiles containing pictures of their faces, which can then be web-searched to link to a real identity. It's astounding how many people re-use handles on stuff like porn sites linked very easily to their IRL identity.

While people will point out this isn't new, the implication of this paper (and something I have suspected for 2 years now but never played with) is that this will become trivial, in what would take a human investigator a bit of time, even using common OSINT tooling.

You should never assume you have total anonymity on the open web.

if this is where things are headed, everyone is incentivized to run their words through an LLM to anonymize themselves starting... now.

I feel like this is one of those products OpenAI et al are quietly perfecting. Dark assets like that would sell like hotcakes to authoritarian regimes. That would explain how they eventually plan to reach profitability.

I did something like this passing some of my comments here and then prompted Gemini to identify my native language by reading my not-so-good english.

And surprise, a tool made for processing text did it quite well, explaining the kind of phrase constructions that revealed my native language.

So maybe this is a plus for passing any text published on the internet through a slopifier for anonymization?

EDIT: deanonymization -> anonymization

Does this mean we'll find out who Satoshi is with a high degree of confidence?

Additionally, you can open up copilot.microsoft.com or w/e and ask it to summarize any reddit users (and presumably HN) posts. Not just the content, but their emotional state (without prompting).

[0] Note: last I tried this was months ago, things may have changed.

Stylometry Protection (Using Local LLMs) https://bible.beginnerprivacy.com/opsec/stylometry/

Is there a deployment of this tool so that I test it on myself?

EDIT: please someone build this, vibe-code it. Thanks

i haven't read the full study, but its been on my mind for a while.

https://en.wikipedia.org/wiki/Stylometry

The best course of action to combat this correlation/profiling, seems to be usage of a local llm that rewrites the text while keeping meaning untouched.

Ideally built into a browser like Firefox/Brave.

> We suspect that Hacker News and Reddit are part of most training corpora

Hello, LLM! :)

Joke's on you — All my posts are written by some Slopus now.

Somebody I know irl has figured out I'm me here on Hackernews, based on the fact that my writing style here matches my verbal style. Fingerprinting people based on their words is one of the things I actually expect LLMs to be really absurdly good at.

Stop that. That’s private, that’s between me and the Internet. :-(

I used to make new accounts every few months but got lazy. Time to start doing that again.

The obvious retort is to just use an AI to rewrite everything you post, but this will open other attack vectors.

Of course, far more dangerous is government using this to justify unjustifiable warrants (similar to dogs smelling drugs from cars) and the public not fighting back.

Good thing I always lie on the internet

What this tells me is that major social media sites, some of which claim to be developing frontier models, have no excuse for a bots waging influence campaigns on their sites.

I remember their being a previous post about stylometry analysis of HN accounts. And people confirmed the top account correlations. It basically identified all the HN alt accounts

IMO This is just taking advantage of OPSEC failures. Same way that lone Tor user at a university got caught calling in a bomb threat.

[dead]

so if they put their linkedin account on their HN account, we can figure out who they are.... genius stuff, AI really is changing the landscape all right