Academic Report



时间:9月18日 9:30—12:00




PPSB: An Open and Flexible Platform for Privacy-Preserving Safe Browsing

报告摘要:Safe Browsing (SB) is one of the important security features in modern web browsers to help detect new unsafe websites. Despite useful, recent studies have pointed out that the widely adopted SB services, such as 谷歌 Safe Browsing, 微软 SmartScreen, Opera Fraud and Malware Protection and etc., can raise privacy concerns since users' browsing history might be subject to unauthorized leakage to service providers.

In this talk, I will present a Privacy-Preserving Safe Browsing (PPSB) platform. It bridges the client application that uses the service and the blacklist providers who provide unsafe URLs, with the guaranteed privacy of users and third-party blacklist providers. Particularly, in PPSB, the actual URL to be checked, as well as its associated hashes or hash prefixes, never leave the client application in cleartext, i.e., no information about the URL can be revealed from the masked request. This protects user's browsing history from being directly leaked or indirectly inferred. Moreover, these lists of unsafe URLs, which could be the most valuable asset for the blacklist providers, are always encrypted and kept private within the PPSB platform. We provide well-defined APIs for client applications to vet a URL and blacklist providers to deploy the server. Based on these APIs, we implement a prototype of the client application as a Chrome extension, and an easy-to-deploy Docker image for blacklist providers to privately contribute their encrypted lists of unsafe URLs, as well as set up their own PPSB servers on demand. Extensive evaluations using real datasets (with more than 1 million unsafe URLs) demonstrate that our prototype can function as intended without sacrificing normal user experience, and block unsafe URLs at the millisecond level. To engage the community, we release the Chrome extension and the Docker image in Chrome Web Store and Docker Hub respectively, and the source code is available on GitHub.


Dr. Helei Cui is currently a Senior Research Associate in the Department of Computer Science, City University of Hong Kong. He has finished his Ph.D. study in Computer Science from City University of Hong Kong in 2018, and has received M.S. degree in Information Engineering from The Chinese University of Hong Kong in 2013 and B.E. degree in Software Engineering from Northwestern Polytechnical University in 2010. His current research interests include data and computation outsourcing security in the context of cloud computing, blockchain and decentralized application, multimedia security, and mobile security. He is a member of IEEE.

2报告题目:Data integrative analysis models for modular patterns discovery






张炯博士,20126月于西北农林科技大学信息工程威尼斯626767com获硕士学位,并于20173月获荷兰埃因霍芬理工大学博士学位,现于美国南加州大学凯克医学中心所属的神经影像学和信息学研究所进行博士后研究。博士期间,主要从事基于微分几何的多方向多尺度的视网膜图像分析,以及在威尼斯人官网辅助诊断方面的应用。博士期间参与了欧盟第七框架计划(FP7)-欧洲研究委员会(ERC)启动基金-基于李群分析的图像处理,以及中荷国际科技合作专项基金-基于激光扫描和层析成像的眼部疾病筛查方案,设计开发了一系列针对视网膜血管分析方面的理论算法模型。博士后期间主要从事基于深度学习技术的三维光学相干断层扫描血管造影图像(OCT-A)的分析及相关疾病的智能诊断。近五年以第一编辑和合作编辑身份共发表论文20余篇,其中以主要编辑在《IEEE Transactions on Medical Imaging》,《IEEE Transactions on Image Processing,IEEE Transactions on Biomedical Engineering》和《Pattern Recognition 等国际顶级期刊上发表论文7篇。并担任顶级期刊IEEE-TMIIEEE-TIP10余个国际期刊和国际成像顶级会议MICCAI 的审稿人。

4报告主题:Privacy-Preserving and Authentication Protocols in Wireless Networks


My current research work focuses on Authentication and Key Agreement Protocol in WSNs, Conditional Privacy-Preserving Authentication Protocol for VANETs, Privacy-Preserving Authentication Protocol for Mobile Cloud/Fog Services, and Secure Data Deduplication Protocol for Fog-assisted Mobile CrowdSensing.

In this presentation, I will present our work upon Conditional Privacy-Preserving Authentication Protocol for vehicular ad hoc networks (VANETs).  

Unlike wired networks, VANETs are subject to a broader range of attacks due to its wireless broadcast nature. One of the potential cryptographic solutions to ensure authentication and privacy preservation is conditional privacy-preserving authentication (CPPA) schemes.  Although a number of CPPA schemes have been proposed in the literature, existing approaches generally suffer from limitations such as  the security

problem of system private keys, high computation requirement during certificate generation and message verification phases. To resolve these issues, we present a provably-secure CPPA scheme for VANETs and demonstrates that the proposed solution provides both security and privacy required in a VANET application. It also demonstrates its utility in terms of computation and communication overheads and owns an optimal performance compared with rather related schemes.

报告人概况:JiLiang Li (李吉亮) is currently a PhD student in Institute of Computer Science, University of Goettingen, Goettingen, Germany.

He received the Bachelor degree in Information Management and Information System from Shaanxi Normal University, Xi'an, China in 2011, and received the Master degree in Computer Software and Theory from Shaanxi Normal University, Xi'an, China in 2015.

From Aug. 2011 to Aug. 2012, he worked as a volunteer teacher in GanSu Province, China.

From Sep. 2013 to Aug. 2014, he studied in the Department of Computer Science and Technology, Tsinghua University, China, where he was a joint-training master student.

From Sep. 2014 to Jan. 2015, he studied in the Visual Computing Center, King Abdullah University of Science and Technology, Saudi Arabia, where he was a visiting master student.

From Jul. 2015 to Aug. 2016, he worked in the State Key Laboratory of Integrated Services Networks, Xidian University, China, where he was a research assistant.  

From Oct. 2018-Dec. 2018, he will study in National Institute of Informatics, Japan, where he would be a visiting PhD.  

His research interests include wireless/mobile network security, information security and cryptography.

5Title Exploiting Clustering Manifold Structure for Hyperspectral Imagery Super-Resolution


Fusing a low-resolution hyperspectral image (HSI) with a high-resolution (HR) conventional image into an HR HSI has become a prevalent HSIs super-resolution scheme. However, in most previous works, few attention has been paid on exploiting the underlying manifold structure in spatial domain of the latent HR HSI. In this study, we advance a provable prior knowledge that the clustering manifold structure of the latent HSI can be well preserved in the spatial domain of the input conventional image. Inspired by this, we first conduct clustering in the spatial domain of the input conventional image and adopt the intra-cluster self-expressiveness model to implicitly depict the clustering manifold structure, which enables learning the complicated manifold structure via solving a constrained ridge regression model without knowing the exact form of the manifold. Then, we incorporate the learned structure into a variational super-resolution framework to regularize the latent HSI. The resulted framework can be effectively optimized by a standard alternating direction method of multipliers (ADMM). Since the learned structure can well depict the underlying spatial manifold of the latent HSI, the proposed method shows the state-of-the-art super-resolution performance on two benchmark datasets.

Biography: Lei Zhang is a research staff in The University of Adelaide. He has received his B.S. and Ph.D. degrees from Northwestern Polytechnical University, China. His research mainly focuses on image processing, transfer learning and few/zero-shot learning etc. He has published over 30 papers in academic journals and conferences, including International Journal of Computer Vision (IJCV), IEEE Transactions on Image Processing (TIP), IEEE Transactions on Geoscience and Remote Sensing (TGRS), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), CVPR, ICCV and ECCV etc. He also serves as reviewer for journals, including IEEE Transactions on Cybernetics (TCB), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Geoscience and Remote Sensing (TGRS), IEEE Transactions on Signal Processing (TSP), IEEE Journal of Selected Topics in Signal Processing (TSTP), Pattern Recognition (PR) and Neurocomputing etc.


Performance Engineering and Low Latency System





商磊,本科和硕士毕业于西北工业大学,2012年于新南威尔士大学获得博士学位。曾任职于中国科威尼斯626767com计算技术研究所、澳大利亚国立大学国家计算中心等知名研究机构,先后担任助理研究员、研究员、高级研究员。先任职于国际知名量化交易企业海纳国际集团(SIG) ,担任亚太区系统技术负责人。商磊博士是美国威尼斯人官网协会和澳大利亚威尼斯人官网协会的资深会员,参与了中澳最快超级威尼斯人官网的研发和调优工作,其研究领域涉及高性能威尼斯人官网系统优化、静态程序分析和超低延迟系统。


Haptic Interaction and Future 4D Haptic Displays


In addition to visual sensation and auditory sensation, various haptic sensation and perception is one of important human abilities to interact with our physical and cyber world. The haptic interaction improves user experience while interacting with electronic systems via visual and/or auditory channel. In this talk, I will make a state-of-the-art report about haptic interaction, and present my previous research work on designing and developing various haptic systems and applications, such as tactile maps on pin-arrayed haptic displays, and how to represent other type of spatial information via haptic displays. In the end of the talk, I will introduce a new research trend on haptic shape change displays, and my current work to design a future 4D haptic displays.


曾丽敏博士,男,江西南昌人,2013年获得德国德累斯顿工业大学威尼斯人官网人机交互博士学位。2014-2015年留校从事博士后研究,期间成果申请到欧盟FP7项目资助,并以项目负责人参与项目研发。目前,仍就职于德国德累斯顿工业大学,任高级研究员及授课讲师。研究方向主要涉及多模态人机交互技术、触觉交互、可穿戴智能系统及信息无障碍化。已在本领域重要学术期刊及会议发表20多篇高质量学术论文,并获得移动交互国际一流会议ACM MobileHCI2015荣誉论文奖。同时,长期担任多个学术期刊及国际会议审稿,如IEEE Trans. HMS, IEEE Trans. Haptics, ACM CHI等。


In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals. We study this problem in a relational setting and make the following contributions. First, we compare two different notions of relational marginals. Second, we show a duality between the resulting relational marginal problems and the maximum likelihood estimation of the parameters of relational models, which generalizes a well-known duality from the propositional setting. Third, by exploiting the relational marginal formulation, we present a statistically sound method to learn the parameters of relational models that will be applied in settings where the number of constants differs between the training and test data. Furthermore, based on a relational generalization of marginal polytopes, we characterize cases where the standard estimators based on feature's number of true groundings needs to be adjusted and we quantitatively characterize the consequences of these adjustments. Fourth, we prove bounds on expected errors of the estimated parameters, which allows us to lower-bound, among other things, the effective sample size of relational training data.


王彧弋  2015年博士毕业于比利时鲁汶大学,现于苏黎世瑞士联邦理工从事研究工作,研究兴趣主要是理论威尼斯人官网科学与机器学习。

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