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Experiments and Analyses of Anonymization Mechanisms for Trajectory Data Publishing

Journal of Computer Science & Technology 2022年第5期37卷 1026-1048页

作者：孙设马帅宋景和岳文海林学练马铁军 State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang UniversityBeijing 100191China Department of Decision Analytics and Risk Southampton Business SchoolUniversity of Southampton Southampton SO171BJU.K

With the advancing of location-detection technologies and the increasing popularity of mobile phones and other location-aware devices,trajectory data is continuously *** large-scale trajectories provide opportunities for various applications,the locations in trajectories pose a threat to individual ***,there has been an interesting debate on the reidentifiability of individuals in the Science *** main finding of Sánchez et *** exactly opposite to that of De Montjoye et al.,which raises the first question:"what is the true situation of the privacy preservation for trajectories in terms of reidentification?''Furthermore,it is known that anonymization typically causes a decline of data utility,and anonymization mechanisms need to consider the trade-off between privacy and *** raises the second question:"what is the true situation of the utility of anonymized trajectories?''To answer these two questions,we conduct a systematic experimental study,using three real-life trajectory datasets,five existing anonymization mechanisms(i.e.,identifier anonymization,grid-based anonymization,dummy trajectories,k-anonymity andε-differential privacy),and two practical applications(i.e.,travel time estimation and window range queries).Our findings reveal the true situation of the privacy preservation for trajectories in terms of reidentification and the true situation of the utility of anonymized trajectories,and essentially close the debate between De Montjoye et *** Sánchez et *** the best of our knowledge,this study is among the first systematic evaluation and analysis of anonymized trajectories on the individual privacy in terms of unicity and on the utility in terms of practical applications.

关键词： anonymization privacy reidentification trajectory utility

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DRNet:Towards fast,accurate and practical dish recognition

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Science China(Technological Sciences) 2021年第12期64卷 2651-2661页

作者： CHENG SiYuan CHU BinFei ZHONG BiNeng ZHANG ZiKai LIU Xin TANG ZhenJun LI XianXian Guangxi Key Lab of Multi-Source Information Mining&Security Guangxi Normal UniversityGuilin 541004China Department of Computer Science and Technology Huaqiao UniversityXiamen 361021China Seetatech Technology Nanjing 211800China

Existing algorithms of dish recognition mainly focus on accuracy with predefined classes,thus limiting their application *** this paper,we propose a practical two-stage dish recognition framework(DRNet)that yields a tradeoff between speed and accuracy while adapting to the variation in class *** the first stage,we build an arbitrary-oriented dish detector(AODD)to localize dish position,which can effectively alleviate the impact of background noise and pose *** the second stage,we propose a dish reidentifier(DReID)to recognize the registered dishes to handle uncertain *** further improve the accuracy of DRNet,we design an attribute recognition(AR)module to predict the attributes of *** attributes are used as auxiliary information to enhance the discriminative ability of ***,pruning and quantization are processed on our model to be deployed in embedded ***,to facilitate the study of dish recognition,a well-annotated dataset is *** AODD,DReID,AR,and DRNet run at about 14,25,16,and 5 fps on the hardware RKNN 3399 pro,respectively.

关键词： neural network acceleration neural network quantization object detection reidentification dish recognition

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