"We show that LLM agents can figure out who you are from your anonymous online posts. Across Hacker News, Reddit, LinkedIn, and anonymized interview transcripts, our method identifies users with high precision - and scales to tens of thousands of candidates."
"While it has long been known that individuals can be identified using only a few data points, doing so was often impractical. Such data often existed in an unstructured form and it took considerable effort for human investigators to assemble enough pieces to solve the identity puzzle."
"Large language models fundamentally change this calculus, enabling fully automated deanonymization attacks that operate at scale and affordably, transforming what was once a labor-intensive manual process into an efficient, automated threat to online anonymity."
Large language models present a significant threat to online privacy by enabling automated deanonymization of internet users, even those using pseudonyms. Building on Latanya Sweeney's foundational 2002 research demonstrating that 87 percent of the US population could be identified using just three data points, researchers have shown that LLMs can now automate the deanonymization process across platforms like Hacker News, Reddit, and LinkedIn. While identifying individuals from anonymous data has been theoretically possible for decades, the process was impractical and labor-intensive. LLMs fundamentally change this by automating and scaling deanonymization attacks affordably, making privacy breaches more feasible and widespread than ever before.
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