{"563101":{"#nid":"563101","#data":{"type":"event","title":"ARC Colloquium: Sofya Raskhodnikova (Penn State)","body":[{"value":"\r\n\u003Cp style=\u0022color:maroon;\u0022\u003EVideo of this talk is available at: \u003Ca href=\u0022https:\/\/smartech.gatech.edu\/handle\/1853\/56017\u0022\u003Ehttps:\/\/smartech.gatech.edu\/handle\/1853\/56017\u003C\/a\u003E\u003C\/p\u003E\r\nFull collection of talk videos are available at:  \r\n\u003Ca href=\u0022https:\/\/smartech.gatech.edu\/handle\/1853\/46836\u0022\u003Ehttps:\/\/smartech.gatech.edu\/handle\/1853\/46836\u003C\/a\u003E\r\n\r\n\u003Cbr\u003E\r\n\u003Cbr\u003E\r\n\r\n\u003Cp  align=\u0022center\u0022\u003E\u003Cstrong\u003EAlgorithms \u0026amp; Randomness Center (ARC)\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align=\u0022center\u0022\u003E\u003Ca href=\u0022http:\/\/www.cse.psu.edu\/~sxr48\/\u0022\u003E\u003Cstrong\u003ESofya Raskhodnikova - Penn State\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align=\u0022center\u0022\u003E\u003Cstrong\u003EMonday, November 7, 2016\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align=\u0022center\u0022\u003E\u003Cstrong\u003EKlaus 1116 East - 11am\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u003Cem\u003EDifferentially Private Analysis of Graphs\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u003Cbr \/\u003E\r\nMany types of data can be represented as graphs, where nodes correspond to individuals and edges capture relationships between them. Examples include datasets capturing \u0026ldquo;friendships\u0026rdquo; in an online social network, financial transactions, email communication, doctor-patient relationships, and romantic ties. On one hand, such datasets contain sensitive information about individuals. On the other hand, global information that can be gleaned from their analysis can provide significant benefits to society. Several naive attempts at anonymizing sensitive data by stripping obvious identifying information resulted in spectacular failures. In this talk, we discuss algorithms for analyzing network data that satisfy a rigourous notion of privacy called\u003Cem\u003E\u0026nbsp;node differential privacy\u003C\/em\u003E. We present several techniques for designing node differentially private algorithms, based on combinatorial analysis, network flow, and linear and convex programming.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EBased on joint work with A. Smith (FOCS 2016) and with S. Kasiwisvanathan, K. Nissim, A. Smith (TCC 2013)\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u003Ca href=\u0022http:\/\/www.cse.psu.edu\/~sofya\/\u0022 target=\u0022_blank\u0022\u003ESofya\u0026nbsp;Raskhodnikova\u003C\/a\u003E\u0026nbsp;is an associate professor of Computer Science and Engineering at Penn State. Her research interests include sublinear-time algorithms, private data analysis, approximation algorithms, and randomized algorithms. She got her PhD from MIT in 2003. She has held visiting positions at\u0026nbsp;\u003Ca href=\u0022http:\/\/www.huji.ac.il\/\u0022 target=\u0022_blank\u0022\u003Ethe Hebrew University of Jerusalem\u003C\/a\u003E,\u0026nbsp;\u003Ca href=\u0022http:\/\/www.weizmann.ac.il\/\u0022 target=\u0022_blank\u0022\u003Ethe Weizmann Institute of Science\u003C\/a\u003E,\u0026nbsp;\u003Ca href=\u0022http:\/\/www.ipam.ucla.edu\/\u0022 target=\u0022_blank\u0022\u003Ethe Institute for Pure and Applied Mathematics\u003C\/a\u003E,\u0026nbsp;\u003Ca href=\u0022http:\/\/www.bu.edu\/cs\/busec\/people\/\u0022 target=\u0022_blank\u0022\u003EBoston University\u003C\/a\u003E\u0026nbsp;and\u0026nbsp;\u003Ca href=\u0022http:\/\/www.seas.harvard.edu\/computer-science\u0022 target=\u0022_blank\u0022\u003EHarvard University\u003C\/a\u003E.\u0026nbsp;\u003Cbr \/\u003E\r\nSpeaker\u0026#39;s webpage: \u003Ca href=\u0022http:\/\/www.cse.psu.edu\/~sofya\/\u0022 target=\u0022_blank\u0022\u003ESofya Raskhodnikova\u003C\/a\u003E\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Differentially Private Analysis of Graphs (Klaus 1116 E at 11am)"}],"uid":"27466","created_gmt":"2016-08-16 10:16:58","changed_gmt":"2017-04-13 21:15:07","author":"Dani Denton","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2016-11-07T11:00:00-05:00","event_time_end":"2016-11-07T12:00:00-05:00","event_time_end_last":"2016-11-07T12:00:00-05:00","gmt_time_start":"2016-11-07 16:00:00","gmt_time_end":"2016-11-07 17:00:00","gmt_time_end_last":"2016-11-07 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"70263","name":"ARC"},{"id":"47223","name":"College of Computing"},{"id":"50875","name":"School of Computer Science"}],"categories":[],"keywords":[{"id":"111051","name":"Algorithm and Randomness Center"},{"id":"4265","name":"ARC"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EDani Denton\u003C\/p\u003E\r\n\r\n\u003Cp\u003Edenton at cc dot gatech dot edu\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}