{"url":"/dataset/iitm-bandersnatch","name":"IITM-Bandersnatch","full_name":null,"description_markdown":"**IITM-Bandersnatch** is a dataset to evaluate traffic analysis techniques. The dataset comprises of data points of the form {encrypted traces, ground truth choices}. To collect each data point, we asked the viewer to watch Bandersnatch from the beginning and note down the choices made by them. At the same time, we collected the encrypted network traffic.\r\nAs of now, our dataset contains information corresponding to 100 viewers who volunteered for this study.","description_withheld":null,"homepage":"https://github.com/Gargi-Mitra/SIGCOMM2019-NetflixInteractive","introduced_date":"2019-03-15","introduced_date_note":null,"introduced_by":{"paper":null,"title":"White Mirror: Leaking Sensitive Information from Interactive Netflix Movies using Encrypted Traffic Analysis","first_author":null,"url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["IITM-Bandersnatch"],"data_loaders":[{"repo":"https://github.com/Gargi-Mitra/SIGCOMM2019-NetflixInteractive","url":"https://github.com/Gargi-Mitra/SIGCOMM2019-NetflixInteractive","frameworks":[]}],"num_papers_in_archive":1,"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":"2026-09-25T09:33:49+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."}