Computer Science ›› 2021, Vol. 48 ›› Issue (6): 301-305.doi: 10.11896/jsjkx.201200223
Special Issue: Information Security
• Information Security • Previous Articles Next Articles
WANG Le-ye
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[1]ZHANG D,WANG L,XIONG H,et al.4W1H in Mobile Crowd Sensing[J].IEEE Communications Magazine,2014,52(8):42-48. [2]GANTI R K,YE F,LEI H.Mobile crowdsensing:current state and future challenges[J].IEEE Communications Magazine,2011,49(11):32-39. [3]GUO B,WANG Z,YU Z,et al.Mobile crowd sensing and computing:The review of an emerging human-powered sensing pa-radigm[J].ACM Computing Surveys(CSUR),2015,48(1):1-31. [4]LIU Y,KONG L,CHEN G.Data-oriented mobile crowdsen-sing:A comprehensive survey[J].IEEE Communications Surveys & Tutorials,2019,21(3):2849-2885. [5]WANG J,WANG L,WANG Y,et al.Task allocation in mobile crowd sensing:State-of-the-art and future opportunities[J].IEEE Internet of Things Journal,2018,5(5):3747-3757. [6]VERGARA-LAURENS I J,JAIMES L G,LABRADOR M A.Privacy-preserving mechanisms for crowdsensing:Survey and research challenges[J].IEEE Internet of Things Journal,2016,4(4):855-869. [7]DWORK C.Differential privacy:A survey of results[C]//International Conference on Theory and Applications of Models of Computation.2008:1-19. [8]CORMODE G,JHA S,KULKARNI T,et al.Privacy at scale:Local differential privacy in practice[C]//Proceedings of the 2018 International Conference on Management of Data.2018:1655-1658. [9]ANDRÉS M E,BORDENABE N E,CHATZIKOKOLAKIS K,et al.Geo-indistinguish ability:Differential privacy for location-based systems[C]//Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security.2013:901-914. [10]WANG L,YANG D,HAN X,et al.Location privacy-preserving task allocation for mobile crowdsensing with differential geo-obfuscation[C]//Proceedings of the 26th International Conference on World Wide Web.2017:627-636. [11]TO H,SHAHABI C,XIONG L.Privacy-preserving online task assignment in spatial crowdsourcing with untrusted server[C]//2018 IEEE 34th International Conference on Data Engineering(ICDE).2018:833-844. [12]TO H,SHAHABI C.Location privacy in spatial crowdsourcing[M].Handbook of Mobile Data Privacy,Springer,2018:167-194. [13]BORDENABE N E,CHATZIKOKOLAKIS K,PALAMIDESSI C.Optimal geo-indistinguishable mechanisms for location pri-vacy[C]//Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security.2014:251-262. [14]WANG L,ZHANG D,YANG D,et al.Differential location privacy for sparse mobile crowdsensing[C]//2016 IEEE 16th International Conference on Data Mining(ICDM).2016:1257-1262. [15]TO H,GHINITA G,FAN L,et al.Differentially private location protection for worker datasets in spatial crowdsourcing[J].IEEE Transactions on Mobile Computing,2016,16(4):934-949. [16]CORMODE G,PROCOPIUC C,SRIVASTAVA D,et al.Diffe-rentially private spatial decompositions[C]//2012 IEEE 28th International Conference on Data Engineering.2012:20-31. [17]TO H,GHINITA G,SHAHABI C.A framework for protecting worker location privacy in spatial crowdsourcing[J].Proceedings of the VLDB Endowment,2014,7(10):919-930. [18]GUO B,LIU Y,WU W,et al.Activecrowd:A framework foroptimized multitask allocation in mobile crowdsensing systems[J].IEEE Transactions on Human-Machine Systems,2016,47(3):392-403. [19]MA H,ZHAO D,YUAN P.Opportunities in mobile crowdsensing[J].IEEE Communications Magazine,2014,52(8):29-35. [20]JIN X,ZHANG R,CHEN Y,et al.DPSense:Differentially private crowdsourced spectrum sensing[C]//Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security.2016:296-307. [21]WANG L,QIN G,YANG D,et al.Geographic Differential Privacy for Mobile Crowd Coverage Maximization[C]//Procee-dings of AAAI.2018. [22]WANG L,YANG D,HAN X,et al.Mobile Crowdsourcing Task Allocation with Differential-and-Distortion Geo-Obfuscation[J].IEEE Transactions on Dependable and Secure Computing,2019. [23]WANG L,ZHANG D,WANG Y,et al.Sparse mobilecrowdsensing:challenges and opportunities[J].IEEE Communications Magazine,2016,54(7):161-167. [24]WANG L,ZHANG D,YANG D,et al.Sparse MobileCrowdsensing With Differential and Distortion Location Privacy[J].IEEE Transactions on Information Forensics and Secu-rity,2020,15:2735-2749. [25]YANG Q,LIU Y,CHEN T,et al.Federated machine learning:Concept and applications[J].ACM Transactions on Intelligent Systems and Technology(TIST),2019,10(2):1-19. [26]CHAI D,WANG L,CHEN K,et al.Secure federated matrixfactorization[J].IEEE Intelligent Systems,2020. |
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