New PANOPTICON dataset tackles LLM privacy risks with PII data

Researchers have developed a new pipeline and dataset called PANOPTICON to address the challenge of studying privacy risks in Large Language Models (LLMs). The dataset, generated using Meta's Llama-3.1-8B-Instruct model, contains over 67,000 prompts with Personally Identifiable Information (PII) derived from synthetic user profiles. This benchmark dataset is designed to facilitate research into Prompt Inversion Attacks (PIAs) and quantify privacy leakage within LLM context windows, marking a significant step for LLM privacy research. AI

IMPACT Enables quantitative study of LLM privacy leakage and Prompt Inversion Attacks, potentially leading to more secure LLM deployments.

RANK_REASON The cluster describes a new academic paper introducing a novel dataset and methodology for LLM privacy research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Llama 3.1 8B-Instruct
• Meta
• Mir Mehedi Ahsan Pritom
• PANOPTICON
• Personally Identifiable Information
• Prompt Inversion Attacks
• Stephen Squire

• paper
• safety

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