{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/databright-towards-a-global-exchange-for","title":"DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation","arxiv_id":"1802.04780","date":"2018-02-13","proceeding":null,"authors":["David Dao","Dan Alistarh","Claudiu Musat","Ce Zhang"],"abstract":"It is safe to assume that, for the foreseeable future, machine learning,\nespecially deep learning will remain both data- and computation-hungry. In this\npaper, we ask: Can we build a global exchange where everyone can contribute\ncomputation and data to train the next generation of machine learning\napplications?\n  We present an early, but running prototype of DataBright, a system that turns\nthe creation of training examples and the sharing of computation into an\ninvestment mechanism. Unlike most crowdsourcing platforms, where the\ncontributor gets paid when they submit their data, DataBright pays dividends\nwhenever a contributor's data or hardware is used by someone to train a machine\nlearning model. The contributor becomes a shareholder in the dataset they\ncreated. To enable the measurement of usage, a computation platform that\ncontributors can trust is also necessary. DataBright thus merges both a data\nmarket and a trusted computation market.\n  We illustrate that trusted computation can enable the creation of an AI\nmarket, where each data point has an exact value that should be paid to its\ncreator. DataBright allows data creators to retain ownership of their\ncontribution and attaches to it a measurable value. The value of the data is\ngiven by its utility in subsequent distributed computation done on the\nDataBright computation market. The computation market allocates tasks and\nsubsequent payments to pooled hardware. This leads to the creation of a\ndecentralized AI cloud. Our experiments show that trusted hardware such as\nIntel SGX can be added to the usual ML pipeline with no additional costs. We\nuse this setting to orchestrate distributed computation that enables the\ncreation of a computation market. DataBright is available for download at\nhttps://github.com/ds3lab/databright.","url_abs":"http://arxiv.org/abs/1802.04780v1","url_pdf":"http://arxiv.org/pdf/1802.04780v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"databright-towards-a-global-exchange-for","repo_url":"https://github.com/ds3lab/databright","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}