{"url":"/dataset/confaide","name":"ConfAIde","full_name":null,"description_markdown":"ConfAIde is a benchmark that evaluates the inference-time privacy implications of Language Models (LLMs) in interactive settings.","description_withheld":null,"homepage":"https://confaide.github.io/","introduced_date":"2023-10-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/can-llms-keep-a-secret-testing-privacy","title":"Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory","first_author":"Niloofar Mireshghallah","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["ConfAIde"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}