With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dyn...With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtuMized resources are provided as services. With virtualization technology, cloud computing offers diverse services （such as virtual computing, virtual storage, virtual bandwidth, etc.） for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users＇ embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.展开更多
Although recommendation techniques have achieved distinct developments over the decades,the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality.Most of the ex...Although recommendation techniques have achieved distinct developments over the decades,the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality.Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings.How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge.In this paper,based on a factor graph model,we formalize the problem in a semi-supervised probabilistic model,which can incorporate different user information,user relationships,and user-item ratings for learning to predict the unknown ratings.We evaluate the method in two different genres of datasets,Douban and Last.fm.Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms.Furthermore,a distributed learning algorithm is developed to scale up the approach to real large datasets.展开更多
基金supported by National Basic Research Program of China (973 Program) (No. 2007CB310800)China Postdoctoral Science Foundation (No. 20090460107 and No. 201003794)
文摘With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtuMized resources are provided as services. With virtualization technology, cloud computing offers diverse services （such as virtual computing, virtual storage, virtual bandwidth, etc.） for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users＇ embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.
基金supported by the National Natural Science Foundation of China(Nos.61035004,61273213,61072043,and 61305055)the National Defense Science Foundation of China(No.9140A15090112JB93180)
文摘Although recommendation techniques have achieved distinct developments over the decades,the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality.Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings.How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge.In this paper,based on a factor graph model,we formalize the problem in a semi-supervised probabilistic model,which can incorporate different user information,user relationships,and user-item ratings for learning to predict the unknown ratings.We evaluate the method in two different genres of datasets,Douban and Last.fm.Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms.Furthermore,a distributed learning algorithm is developed to scale up the approach to real large datasets.
