{"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/dpugc-learn-differentially-private","title":"dpUGC: Learn Differentially Private Representation for User Generated Contents","arxiv_id":"1903.10453","date":"2019-03-25","proceeding":null,"authors":["Xuan-Son Vu","Son N. Tran","Lili Jiang"],"abstract":"This paper firstly proposes a simple yet efficient generalized approach to\napply differential privacy to text representation (i.e., word embedding). Based\non it, we propose a user-level approach to learn personalized differentially\nprivate word embedding model on user generated contents (UGC). To our best\nknowledge, this is the first work of learning user-level differentially private\nword embedding model from text for sharing. The proposed approaches protect the\nprivacy of the individual from re-identification, especially provide better\ntrade-off of privacy and data utility on UGC data for sharing. The experimental\nresults show that the trained embedding models are applicable for the classic\ntext analysis tasks (e.g., regression). Moreover, the proposed approaches of\nlearning differentially private embedding models are both framework- and data-\nindependent, which facilitates the deployment and sharing. The source code is\navailable at https://github.com/sonvx/dpText.","url_abs":"http://arxiv.org/abs/1903.10453v1","url_pdf":"http://arxiv.org/pdf/1903.10453v1.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":"dpugc-learn-differentially-private","repo_url":"https://github.com/sonvx/dpText","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.10453","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}