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計算機科學與工程學院系列學術報告——李罡 澳大利亞迪肯大學

作者:常婉綸 審核人: 點擊:[]

報告題目:Privacy aware Data Science

報告時間:2020年12月4日 周五 下午2:30

騰訊會議ID: 979 496 572

報告人:李罡 博士 澳大利亞迪肯大學信息技術學院

專家簡介:Gang Li,男,澳大利亞迪肯大學信息技術學院研究員,澳大利亞迪肯大學Data to Intelligence數據智能研究中心主任。

目前擔任IEEE計算智能學會數據挖掘與大數據分析DMTC技術委員會委員(2017-2018副主席)、IEEE SMC學會工業信息系統(TCEIS)技術委員會委員、IEEE Task Force on Educational Data Mining主席 (2020-2023),并且擔任SCI期刊Journal of Travel Research (Sage), Decision Support Systems(Elsevier)、IEEE Access (IEEE)和 Information Discovery & Delivery (Emerald)副編輯和雜志編委。2017/2020知識科學工程與管理國際會議(KSEM2017/2020,CCF C)程序主席和會議主席、2016 IEEE工業信息系統會議(ES2016)程序主席、2016智能信息處理會議(IIP2016)程序主席、2016 BESC會議程序主席、2019知識科學工程與管理國際會議(KSEM2019,CCF C)大會主席 。

報告摘要:This talk will present an overview of recent advances in differential privacy, in the area of data science. After recapping the basic concepts on differential privacy, we will introduce the requirements of differential privacy in different tasks of data science, including PAC learning, objective optimization and unsupervised learning. Then, techniques for bringing differential privacy into those tasks will be categorized as input perturbation, objective perturbation and output perturbation. A strong composition theorem to quantify the privacy loss in the context of data science will be introduced, as an extension of the simple composition and the advance composition theorems.

下一條:陜西師范大學魏俊發、曹睿教授特邀報告

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