{"url":"/dataset/ml-cb","name":"ML-CB","full_name":"ML-CB: Machine Learning Canvas Block","description_markdown":"In this paper, we develop a new privacy enhancing tool: ML-CB—a means of using distinguishable pictorial information combined with underlying website source code to produce accurate and robust machine learning classifiers able to discern fingerprinting (i.e., surreptitious tracking) from non-fingerprinting canvas-based actions.\r\n\r\nThe data introduced in the paper is collected by scraping roughly half a million websites using a custom Google Chrome extension storing information related to the canvas.\r\n\r\nSource: [ML-CB: Machine Learning Canvas Block](https://www.petsymposium.org/2021/files/papers/issue3/popets-2021-0056.pdf)","description_withheld":null,"homepage":"https://doi.org/10.7910/DVN/KAMCYM","introduced_date":"2021-04-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/ml-cb-machine-learning-canvas-block","title":"ML-CB: Machine Learning Canvas Block","first_author":"Nathan Reitinger","url":null},"license":{"name":"MIT License","url":"https://osf.io/shbe7/"},"modalities":[],"tasks":[],"languages":[],"variants":["ML-CB"],"data_loaders":[],"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":"2026-09-24T18:15:14+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."}