{"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/differentially-private-two-party-egocentric","title":"Differentially-Private Two-Party Egocentric Betweenness Centrality","arxiv_id":"1901.05562","date":"2019-01-16","proceeding":null,"authors":["Leyla Roohi","Benjamin I. P. Rubinstein","Vanessa Teague"],"abstract":"We describe a novel protocol for computing the egocentric betweenness\ncentrality of a node when relevant edge information is spread between two\nmutually distrusting parties such as two telecommunications providers. While\neach node belongs to one network or the other, its ego network might include\nedges unknown to its network provider. We develop a protocol of\ndifferentially-private mechanisms to hide each network's internal edge\nstructure from the other; and contribute a new two-stage stratified sampler for\nexponential improvement to time and space efficiency. Empirical results on\nseveral open graph data sets demonstrate practical relative error rates while\ndelivering strong privacy guarantees, such as 16% error on a Facebook data set.","url_abs":"http://arxiv.org/abs/1901.05562v1","url_pdf":"http://arxiv.org/pdf/1901.05562v1.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":"differentially-private-two-party-egocentric","repo_url":"https://github.com/anusii/graph-dp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"differentially-private-two-party-egocentric","repo_url":"https://github.com/michaelpatrickpurcell/graph-dp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.05562","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}