Tianqing Zhu¹, Mengmeng Yang¹, Ping Xiong², Yang Xiang¹, Wanlei Zhou¹

Computer Systems Science and Engineering, Vol.33, No.2, pp. 61-69, 2018, DOI:10.32604/csse.2018.33.061

Abstract Online social networks provide an unprecedented opportunity for researchers to analysis various social phenomena. These network data is normally represented as graphs, which contain many sensitive individual information. Publish these graph data will violate users’ privacy. Differential privacy is one of the most influential privacy models that provides a rigorous privacy guarantee for data release. However, existing works on graph data publishing cannot provide accurate results when releasing a large number of queries. In this paper, we propose a graph update method transferring the query release problem to an iteration process, in which a large More >

Haitao Zou^{*, 1}, Yifan He¹, Shang Zheng¹, Hualong Yu¹, Chunlong Hu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.115, No.2, pp. 217-231, 2018, DOI:10.3970/cmes.2018.00194

Abstract There are some scenarios that need group recommendation such as watching a movie or a TV series, selecting a tourist destination, or having dinner together. Approaches in this domain can be divided into two categories: Creating group profiles and aggregating individual recommender list. Yet none of the above methods can handle the online group recommendation both efficiently and accurately and these methods either strongly limited by their application environment, or bring bias towards those users having limited connections with this group. In this work, we propose a local optimization framework, using sub-group profiles to compute… More >

Ying Li¹, Jianbo Li^1,*, Jianwei Chen¹, Minchao Lu¹, Caoyuan Li^2,3

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 571-587, 2018, DOI:10.32604/cmc.2018.02851

Abstract The explosive growth of mobile data demand is becoming an increasing burden on current cellular network. To address this issue, we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic. The principle behind it is to select a few important users as seeds for data sharing. The three critical steps are detailed as follows. We first explore individual interests of users by the construction of user profiles, on which an interest graph is built by Gaussian graphical modeling. We then apply the extreme value theory to threshold the encounter duration of user More >