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BVDFed:Byzantine-resilient and verifiable aggregation for differentially private federated learning
《Frontiers of Computer Science》2024年第5期171-187,共17页Xinwen GAO Shaojing FU Lin LIU Yuchuan LUO 
This work was supported by the National Natural Science Foundation of China(Grant Nos.62072466,62102430,62102429,62102422,U1811462);Natural Science Foundation of Hunan Province,China(No.2021JJ40688);Science Research Plan Program by NUDT(No.ZK22-50).
Federated Learning(FL)has emerged as a powerful technology designed for collaborative training between multiple clients and a server while maintaining data privacy of clients.To enhance the privacy in FL,Differentiall...
关键词：federated learning differential private verifiable aggregation Byzantine fault-tolerance 
Locally differentially private frequency distribution estimation with relative error optimization
《Frontiers of Computer Science》2024年第5期227-229,共3页Ning WANGI Yifei LIU Zhigang WANG Zhiqiang WEI Ruichun TANG Peng TANG Ge YU 
This work was supported by the National Natural Science Foundation of China(Grant Nos.61902365,61902366 and 62002203);the Shandong Provincial Natural Science Foundation(No.ZR2020QF045);the Open Project Program from Key Lab of Cryptologic Technology and Information Security(Ministry of Education);Shandong University,and the Young Scholars Program of Shandong University.
This paper tackles the problem of estimating the frequency distribution on the crowdsourced multidimensional categorical data under local differential privacy(LDP).Although the frequency estimation problem under LDP[1...
关键词：DISTRIBUTION ESTIMATION ERROR 
An optimal differentially private data release mechanism with constrained error被引量：1
《Frontiers of Computer Science》2022年第1期187-189,共3页Hao WANG Zhengquan XU Xiaoshan ZHANG Xiao PENG Kaiju LI 
This work was supported in part by the National Natural Science Foundation of China(Grant No.42001398);Natural Science Foundation of Chongqing(cstc2020jcyj-msxmX0635);China Postdoctoral Science Foundation funded project(2021M693929);Science and Technology Research Project of CEC(KJQN201900612);Open Fund of LIESMARS(20S02);PhD Starts Fund of CQUPT(A2019-302);SRTP of CQUPT(A2019-175,A2020-106).
1 Introduction and main contributions In data-driven applications,such as location based services(LBSs),disease surveillance and social networks,etc.,information fusion is necessary for data owners to obtain better se...
关键词：LBS RELEASE OPTIMAL 
DP-UserPro:differentially private user profile construction and publication
《Frontiers of Computer Science》2021年第5期197-206,共10页Zheng HUO Ping HE Lisha HU Huanyu ZHAO 
the National Natural Science Foundation of China(Grant No.62002098);Natural Science Foundation of Hebei Province(F2020207001,F2019207061);the Scientific Research Projects of Hebei Education Department(QN2018116);the Research Foundation of Hebei University of Economics and Business(2018QZ04,2019JYQ08).
User profiles are widely used in the age of big data.However,generating and releasing user profiles may cause serious privacy leakage,since a large number of personal data are collected and analyzed.In this paper,we p...
关键词：user profile DP-CLIQUE CLUSTERING differential privacy recommender system 
Differentially private high-dimensional data publication via grouping and truncating techniques被引量：4
《Frontiers of Computer Science》2019年第2期382-395,共14页Ning WANG Yu GU Jia XU Fangfang LI Ge YU 
the National Natural Science Foundation of China (Grant Nos. 61433008, 61472071 and U143520006);the Fundamental Research Funds for the Central Universities of China (161604005 and 171605001);the Natural Science Foundation of Liaoning Province (2015020018).
The count of one column for high-dimensional datasets, i.e., the number of records containing this column, has been widely used in nuinerous applications such as analyzing popular spots based on check-in location info...
关键词：differential privacy HIGH-DIMENSIONAL data TRUNCATION optimization GROUPING PENALTY function 
SMT-based query tracking for differentially private data analytics systems
《Frontiers of Computer Science》2018年第6期1192-1207,共16页Chen LUO Fei HE 
supported in part by the National Program on Key Basic Research Project (973Program)(2010CB328003); the National Natural Science Foundation of China (Grant Nos.61672310, 61272001,60903030,91218302); the National Key Technologies R&D Program of China (SQ2012BAJY4052).
Detecting differential expression from RNA-seq data with expression measurement uncertainty被引量：3
《Frontiers of Computer Science》2015年第4期652-663,共12页LiZHANG Songcan CHEN Xuejun LIU 
High-throughput RNA sequencing （RNA-seq） has emerged as a revolutionary and powerful technology for expression profiling. Most proposed methods for detect- ing differentially expressed （DE） genes from RNA-seq are ...
关键词：RNA-SEQ Bayesian method differentially ex-pressed genes/isoforms expression measurement uncer-tainty analysis pipeline 
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