| Title | Distributed Private Online Learning for Social Big Data Computing over Data Center Networks |
| Authors | Li, Chencheng Zhou, Pan Zhou, Yingxue Bian, Kaigui Jiang, Tao Rahardja, Susanto |
| Affiliation | Huazhong Univ Sci & Technol, Sch EIC, Wuhan, Peoples R China. Peking Univ, Sch EECS, Beijing, Peoples R China. Northwestern Polytech Univ, Fremont, CA USA. |
| Keywords | Cloud computing social networks sparse distributed online learning OPTIMIZATION |
| Issue Date | 2016 |
| Publisher | IEEE International Conference on Communications (ICC) |
| Citation | IEEE International Conference on Communications (ICC).2016,392-397. |
| Abstract | With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit knowledge from copious amounts of data obtained and predict social behavior of users, we urge to realize data mining in social networks. Almost all online websites use cloud services to effectively process the large scale of social data, which are gathered from distributed data centers. These data are so largescale, high-dimension and widely distributed that we propose a distributed sparse online algorithm to handle them. Additionally, privacy-protection is an important point in social networks. We should not compromise the privacy of individuals in networks, while these social data are being learned for data mining. Thus we also consider the privacy problem in this article. Our simulations shows that the appropriate sparsity of data would enhance the performance of our algorithm and the privacy-preserving method does not significantly hurt the performance of the proposed algorithm. |
| URI | http://hdl.handle.net/20.500.11897/459821 |
| ISSN | 1550-3607 |
| Indexed | CPCI-S(ISTP) |
| Appears in Collections: | 信息科学技术学院 |
