Online social networks have gradually permeated into every aspect of people's life.As a research hotspot in social network, user influence is of theoretical and practical significant for information transmission, ...Online social networks have gradually permeated into every aspect of people's life.As a research hotspot in social network, user influence is of theoretical and practical significant for information transmission, optimization and integration. A prominent application is a viral marketing campaign which aims to use a small number of targeted infl uence users to initiate cascades of infl uence that create a global increase in product adoption. In this paper, we analyze mainly evaluation methods of user infl uence based on IDM evaluation model, Page Rank evaluation model, use behavior model and some other popular influence evaluation models in currently social network. And then, we extract the core idea of these models to build our influence evaluation model from two aspects, relationship and activity. Finally, the proposed approach was validated on real world datasets,and the result of experiments shows that our method is both effective and stable.展开更多
Cloud computing is very useful for big data owner who doesn't want to manage IT infrastructure and big data technique details. However, it is hard for big data owner to trust multi-layer outsourced big data system...Cloud computing is very useful for big data owner who doesn't want to manage IT infrastructure and big data technique details. However, it is hard for big data owner to trust multi-layer outsourced big data system in cloud environment and to verify which outsourced service leads to the problem. Similarly, the cloud service provider cannot simply trust the data computation applications. At last,the verification data itself may also leak the sensitive information from the cloud service provider and data owner. We propose a new three-level definition of the verification, threat model, corresponding trusted policies based on different roles for outsourced big data system in cloud. We also provide two policy enforcement methods for building trusted data computation environment by measuring both the Map Reduce application and its behaviors based on trusted computing and aspect-oriented programming. To prevent sensitive information leakage from verification process,we provide a privacy-preserved verification method. Finally, we implement the TPTVer, a Trusted third Party based Trusted Verifier as a proof of concept system. Our evaluation and analysis show that TPTVer can provide trusted verification for multi-layered outsourced big data system in the cloud with low overhead.展开更多
Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is ...Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM). The network model consists of two parts, sEEG encoding and source decoding, to model the sEEG signal and receive the regression of source location. As there does not exist enough annotated sEEG signals correspond to specific source locations, simulated data is generated with forward model using finite element method (FEM) to act as a part of training signals. A framework for source localization is proposed to estimate the source position based on simulated training data. Experiments are done on simulated testing data. The results on simulated data exhibit good robustness on noise signal, and the proposed network solves the EEG inverse problem with spatio-temporal deep network. The result show that the proposed method overcomes the highly ill-posed linear inverse problem with data driven learning.展开更多
In recent years,with the rapid development of the Internet of Things(IoT),RFID tags,industrial controllers,sensor nodes,smart cards and other small computing devices are increasingly widely deployed.In order to help p...In recent years,with the rapid development of the Internet of Things(IoT),RFID tags,industrial controllers,sensor nodes,smart cards and other small computing devices are increasingly widely deployed.In order to help protect low-power,low-cost Internet of things devices,lightweight cryptography came into being.In order to launch the standard of cryptographic algorithm suitable for constrained environment,NIST started the process of lightweight cryptography standardization in 2016,and published the second round of candidate cryptographic algorithms in August2019.SKINNY-Hash in the sponge construction is one of the second round candidates,as well as SKINNY-AEAD.The tweakable block cipher SKINNY is the basic component for both of them.Although cryptanalysts have proposed several cryptanalysis results on SKINNY and SKINNY-AEAD,there is no cryptanalysis results on SKINNY-Hash.Based on the differential cryptanalysis and the method of mixed integer programming(MELP),we perform differential cryptanalysis on SKINNY-Hash.The core is to set up the inequations of the MILP model.Actually,it is hard to obtain the inequations of the substitution(i.e.S-box)obeying the previous method.By a careful study of the permutation,we partition the substitution into a nonlinear part and a linear part,then a series of inequations in the MILP model is obtained to describe the differentials with high possibilities.As a result,we propose a differential hash collision path of 3-round SKINNY-tk3-Hash.By adjusting the bit rate of SKINNY-tk3-Hash,we propose a 7-round collision path for the simplified algorithm.The cryptanalysis in this paper will help to promote the NIST Lightweight Crypto Standardization process.展开更多
基金supported by the Research Fund for the Doctoral Program(New Teachers)Ministry of Education of China under Grant No.20121103120032+2 种基金Humanity and Social Science Youth foundation of Ministry of Education of China under Grant No.13YJCZH065General Program of Science and Technology Development Project of Beijing Municipal Education Commission of China under Grant No.km201410005012Open Research Fund of Beijing Key Laboratory of Trusted Computing,Open Research Fund of Key Laboratory of Trustworthy Distributed Computing and Service(BUPT),Ministry of Education
文摘Online social networks have gradually permeated into every aspect of people's life.As a research hotspot in social network, user influence is of theoretical and practical significant for information transmission, optimization and integration. A prominent application is a viral marketing campaign which aims to use a small number of targeted infl uence users to initiate cascades of infl uence that create a global increase in product adoption. In this paper, we analyze mainly evaluation methods of user infl uence based on IDM evaluation model, Page Rank evaluation model, use behavior model and some other popular influence evaluation models in currently social network. And then, we extract the core idea of these models to build our influence evaluation model from two aspects, relationship and activity. Finally, the proposed approach was validated on real world datasets,and the result of experiments shows that our method is both effective and stable.
基金partially supported by grants from the China 863 High-tech Program (Grant No. 2015AA016002)the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20131103120001)+2 种基金the National Key Research and Development Program of China (Grant No. 2016YFB0800204)the National Science Foundation of China (No. 61502017)the Scientific Research Common Program of Beijing Municipal Commission of Education (KM201710005024)
文摘Cloud computing is very useful for big data owner who doesn't want to manage IT infrastructure and big data technique details. However, it is hard for big data owner to trust multi-layer outsourced big data system in cloud environment and to verify which outsourced service leads to the problem. Similarly, the cloud service provider cannot simply trust the data computation applications. At last,the verification data itself may also leak the sensitive information from the cloud service provider and data owner. We propose a new three-level definition of the verification, threat model, corresponding trusted policies based on different roles for outsourced big data system in cloud. We also provide two policy enforcement methods for building trusted data computation environment by measuring both the Map Reduce application and its behaviors based on trusted computing and aspect-oriented programming. To prevent sensitive information leakage from verification process,we provide a privacy-preserved verification method. Finally, we implement the TPTVer, a Trusted third Party based Trusted Verifier as a proof of concept system. Our evaluation and analysis show that TPTVer can provide trusted verification for multi-layered outsourced big data system in the cloud with low overhead.
基金supported by the National Natural Science Foundation of China (No. 61672070, 61501007, 11675199, 61572004 and 81501155)the Key Project of Beijing Municipal Education Commission (No. KZ201910005008)+3 种基金general project of science and technology project of Beijing Municipal Education Commission (No. KM201610005023)the Beijing Municipal Natural Science Foundation (No. 4182005)Clinical Technology Innovation Program of Beijing Municipal Administration of Hospitals (No. XMLX201805)Beijing Municipal Science & Tech Commission (No. Z171100000117004)
文摘Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM). The network model consists of two parts, sEEG encoding and source decoding, to model the sEEG signal and receive the regression of source location. As there does not exist enough annotated sEEG signals correspond to specific source locations, simulated data is generated with forward model using finite element method (FEM) to act as a part of training signals. A framework for source localization is proposed to estimate the source position based on simulated training data. Experiments are done on simulated testing data. The results on simulated data exhibit good robustness on noise signal, and the proposed network solves the EEG inverse problem with spatio-temporal deep network. The result show that the proposed method overcomes the highly ill-posed linear inverse problem with data driven learning.
基金supported by the Natural Science Foundation of Beijing,China(Grant No.4172006)Beijing Municipal Education Commission of China(Grant No.km201410005012)。
文摘In recent years,with the rapid development of the Internet of Things(IoT),RFID tags,industrial controllers,sensor nodes,smart cards and other small computing devices are increasingly widely deployed.In order to help protect low-power,low-cost Internet of things devices,lightweight cryptography came into being.In order to launch the standard of cryptographic algorithm suitable for constrained environment,NIST started the process of lightweight cryptography standardization in 2016,and published the second round of candidate cryptographic algorithms in August2019.SKINNY-Hash in the sponge construction is one of the second round candidates,as well as SKINNY-AEAD.The tweakable block cipher SKINNY is the basic component for both of them.Although cryptanalysts have proposed several cryptanalysis results on SKINNY and SKINNY-AEAD,there is no cryptanalysis results on SKINNY-Hash.Based on the differential cryptanalysis and the method of mixed integer programming(MELP),we perform differential cryptanalysis on SKINNY-Hash.The core is to set up the inequations of the MILP model.Actually,it is hard to obtain the inequations of the substitution(i.e.S-box)obeying the previous method.By a careful study of the permutation,we partition the substitution into a nonlinear part and a linear part,then a series of inequations in the MILP model is obtained to describe the differentials with high possibilities.As a result,we propose a differential hash collision path of 3-round SKINNY-tk3-Hash.By adjusting the bit rate of SKINNY-tk3-Hash,we propose a 7-round collision path for the simplified algorithm.The cryptanalysis in this paper will help to promote the NIST Lightweight Crypto Standardization process.
