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Large-Scale Online Deanonymization with LLMs

simonlermen.substack.com

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Re: Large-Scale Online Deanonymization with LLMs

#51

Post by gmuslera »

ceejayoz wrote: post #37
kseniamorph wrote: post #30

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.

> Most adversaries who would want to deanonymize people at scale (governments, corporations) already have access to far more direct methods. Easier methods probably means more adversaries.

And different agendas. Governments and corporations doesn't try social engineering attacks, scams or do things that end in i.e. ransomware attacks.

Re: Large-Scale Online Deanonymization with LLMs

#52

Post by intended »

gambutin wrote: post #29

Is there a deployment of this tool so that I test it on myself? EDIT: please someone build this, vibe-code it. Thanks

Any tool that can be used for yourself, can be used for others, which is why the researchers wouldn’t release the code/prompt.

That said, give it a few days and someone will have a proof of concept out.

Re: Large-Scale Online Deanonymization with LLMs

#53

Post by DalasNoin »

john_strinlai wrote: post #48

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…

That's a great background paper on the Netflix attack, we make a pretty direct comparison in section 5. We also try to use similar methods for comparison in sections 4 and 6. In section 5 we transform peoples Reddit comments into movie reviews with an LLM and then see if LLMs are better than naraynan purely on movie reviews. LLMs are still much better (getting about 8% but the average person only had 2.5 movies and 48% only shared one movie, so very difficult to match)

Re: Large-Scale Online Deanonymization with LLMs

#55

Post by [deleted] »

joe_mamba wrote: post #41
yomismoaqui wrote: post #38

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: deanonymiza…

>So maybe this is a plus for passing any text published on the internet through a slopifier for deanonymization? Or vice versa, Indian scammers online can now run their traditional Victorian English phrasing through an AI to sound more authentically American. Interviewers now have to deal with remote North Korean deepfaked candidates pretending to be Americans. Just like the internet, AI is now a force multiplier for…

[deleted]

Re: Large-Scale Online Deanonymization with LLMs

#56

Post by john_strinlai »

DalasNoin wrote: post #53
john_strinlai wrote: post #48

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…

That's a great background paper on the Netflix attack, we make a pretty direct comparison in section 5. We also try to use similar methods for comparison in sections 4 and 6. In section 5 we transform peoples Reddit comments into movie reviews with an LLM and then see if LLMs are better than naraynan purely on movie reviews. LLMs are still much better (getting about 8% but the average person only had 2.5 movies and 4…

>we make a pretty direct comparison in section 5

awesome, i saw the mention in the introduction but i havent yet had a chance for a thorough read through of the paper -- ive just skimmed it. looking forward to reading it in-depth!

Re: Large-Scale Online Deanonymization with LLMs

#58

Post by intended »

majorchord wrote: post #28
ranger_danger wrote: post #23

Earlier quoted context omitted.

But you also relied on people giving away too much personal information about themselves... which won't always be the case.

Yeah my first thought was "of course an LLM can do that, we didn't need a paper to tell us". I would be more impressed if it could do it without that information, such as by analyzing writing styles and other cues that aren't direct PII.

It’s the same thing as theft and locks. Any motivated attacker will overcome any rudimentary obstacle. We still use locks because most opportunistic attackers are the most prevalent.

Even the paper on improved phishing showed that LLMs reduce the cost to run phishing attacks, which made previously unprofitable targets (lower income groups), profitable.

The most common deterrent is inconvenience, not impossibility.

Re: Large-Scale Online Deanonymization with LLMs

#59

Post by casey2 »

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.

Re: Large-Scale Online Deanonymization with LLMs

#60

Post by palmotea »

DalasNoin wrote: post #18
mhitza wrote: post #11

Earlier quoted context omitted.

Thanks for the providing the details, where I've been just lazy about reading the paper now :)) I'm not a fan of your proposed changes, as they further lock down platforms. I'd like to see better tools for users to engage with. Maybe if someone is in their Firefox anonymous (or private tab) profile they should be warned when writing about locations, jobs, politics, etc. Even there a small local LLM model would be use…

Mitigations are pretty difficult, I understand it is kind of cool that some websites have really open APIs where you can just read everything. There are some cool apps that used HN data in the past. But I think there should at least be consideration that LLMs are then going to read everything and potentially discover things. Users might have thought this is protected by obscurity, who would read their 5 year old comm…

How helpful would injecting noise and red herring into pseudonymous posts help?

It seems like it would make sense to get in the habit of distort your posts a bit, and do things like make random gender swaps (e.g. s/my husband/my wife), dropping hints that indicate the wrong city (s/I met my friend at Blue Bottle coffee/I met my friend at Coffee Bean), maybe even using an LLM fire off posts indicating false interests (e.g. some total crypto bro thing).

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