With the advent of social networks and cloud computing, the amount of multimedia data produced and communicated within social networks is rapidly increasing. In the meantime, social networking platforms based on cloud computing have made multimedia big data sharing in social networks easier and more efficient. The growth of social multimedia, as demonstrated by social networking sites such as Facebook and YouTube, combined with advances in multimedia content analysis, underscores potential risks for malicious use, such as illegal copying, piracy, plagiarism, and misappropriation. Therefore, secure multimedia sharing and traitor tracing issues have become critical and urgent in social networks. In this article, a joint fingerprinting and encryption (JFE) scheme based on tree-structured Haar wavelet transform (TSHWT) is proposed with the purpose of protecting media distribution in social network environments. The motivation is to map hierarchical community structure of social networks into a tree structure of Haar wavelet transform for fingerprinting and encryption. First, fingerprint code is produced using social network analysis (SNA). Second, the content is decomposed based on the structure of fingerprint code by the TSHWT. Then, the content is fingerprinted and encrypted in the TSHWT domain. Finally, the encrypted contents are delivered to users via hybrid multicast-unicast. The proposed method, to the best of our knowledge, is the first scalable JFE method for fingerprinting and encryption in the TSHWT domain using SNA. The use of fingerprinting along with encryption using SNA not only provides a double layer of protection for social multimedia sharing in social network environment but also avoids big data superposition effect. Theory analysis and experimental results show the effectiveness of the proposed JFE scheme.

With the explosive increase of online images, fast similarity search is increasingly critical for large scale image retrieval. Several hashing methods have been proposed to accelerate image retrieval, a promising way is semantic hashing which designs compact binary codes for a large number of images so that semantically similar images are mapped to similar codes. Supervised methods can handle such semantic similarity but they are prone to overfitting when the labeled data is few or noisy. In this paper, we concentrate on this issue and propose a novel Inductive Transfer Deep Hashing (ITDH) approach for semantic hashing based image retrieval. A transfer deep learning algorithm has been employed to learn the robust image representation, and the neighborhood-structure preserved method has been used to mapped the image into discriminative hash codes in hamming space. The combination of the two techniques ensures that we obtain a good feature representation and a fast query speed without depending on large amounts of labeled data. Experimental results demonstrate that the proposed approach is superior to some state-of-the-art methods.

Video delivery in television white space (TVWS) is a challenging task due to the variation of the spectrum availability in TVWS and the stringent bandwidth requirements of video streaming. Most existing frameworks employ an opportunistic spectrum access style which result in bandwidth inefficiency since only one spectrum hole in TVWS can be used. In this paper, we propose a bandwidth efficient TVWS video delivery framework. First, a multisector structure is employed to improve the bandwidth efficiency. Second, a novel spectrum sensing scheme and an efficient spectrum selection algorithm are proposed to obtain the optimal spectrum holes based on this structure. Third, a joint optimization approach is proposed to create an optimal map between the sectors and the spectrum, and to adapt the video rate commensurate with dynamic channel conditions. The simulation results demonstrate that the proposed framework can obtain significantly higher bandwidth efficiency and superior video performance than traditional approaches.

Digital fingerprinting is a promising approach to protect multimedia content from unauthorized redistribution. However, the existing fingerprints are unsuitable for social network tasks, because they fail to represent the social network structure, which incurs inefficient fingerprint coding. In addition, they are infeasible to efficiently trace colluders due to the large scale of social networks. To address these problems, we design a novel fingerprint, which consists of community relationship code and user identification code. Aiming to preserving the social network structure, we propose a kernelized neighborhood preserving hashing method to generate community relationship codes. The proposed method assigns similar community relationship codes to users in the same or close communities, which improves the anticollusion performance. Because the community relationship codes are binary and neighborhood preserving, they can be used for fast indexing and retrieval. To accelerate the collusion fingerprint tracing, we treat the community relationship codes as index keys to construct a hash table and an inverted index table. Based on the tables, we correspondingly propose an efficient fingerprint detection method. Extensive experiments show that the proposed fingerprint is suitable for social network tasks and the real colluders can be efficiently identified by the proposed fingerprint detection approach.

In this paper, we investigate the capacity of ad hoc wireless networks with directional antennas and multi-packet reception (MPR), which is referred to as DMPR wireless networks hereinafter. MPR has been proven to be an effective technology to improve the capacity by increasing the number of concurrent transmission, however the capacity of MPR networks is constrained by interference. The directional antennas is a straightforward way to mitigate interference and increase the spatial reuse. To further improve the capacity, we firstly introduce the directional antennas into MPR networks, and then conduct the theoretical analysis on the capacity of DMPR wireless networks. Under both arbitrary and random networks, we derive the matching theoretical upper bound and constructive lower bound. The final results demonstrate that a higher capacity can be obtained by such networks.

Video delivery in TV White Space (TVWS) is a challenging task due to the variation of the spectrum availability in TVWS and the stringent bandwidth requirements of video streaming. Most existing frameworks employ an opportunistic spectrum access style which result in bandwidth inefficiency since only one spectrum hole in white space can be used. In this paper, we propose a bandwidth efficient white space video delivery framework. In our framework: firstly, multi-sector structure is employed to improve bandwidth efficiency; secondly, a novel spectrum sensing scheme and an efficient spectrum selection scheme are proposed to obtain optimal spectrum holes based on this structure; thirdly, a optimal resources scheduling scheme is proposed to further improve the system performance. The simulation results demonstrate that the proposed framework can obtain significantly higher bandwidth efficiency and superior video performance than traditional frameworks.

Fast similarity search methods are increasingly critical for many large-scale learning tasks, particularly in the communities of machine learning and data mining. Recently, data-aware hashing method is regarded as a promising approach for similarity search which maps high-dimensional feature vectors into efficient and compact hash codes while preserving the corresponding neighborhood structure. Although some recent hashing methods based on eigenvalue decomposition perform well, they suffer from semantic loss. In this paper, we concentrate on this issue and propose a novel neighborhood preserving hashing approach which adopts a brand-new method to combine non-negative matrix factorization and locality linear embedding without introducing any additional parameter. The combination of these two classical techniques ensures that we obtain a parts-based representation which not only fulfill the psychological and physiological requirements of human perception but also conserve the intrinsic neighborhood structure of the original data. Experiments are conducted to demonstrate that the proposed approach is superior to some state-of-the-art methods.

In this paper, a JFE (joint fingerprinting and encryption) scheme based on the Tree-Structured Harr (TSH) transform is proposed with the purpose of protecting media distribution in social network environment. The motivation is to map hierarchical community structure of social network into tree structure of TSH transform for fingerprinting and encryption. Firstly, the fingerprint code is produced using social network analysis. Secondly, the content is decomposed by the TSH transform. Thirdly, the content is fingerprinted and encrypted in the TSH transform domain. At last, the encrypted contents are delivered to users via hybrid multicast-unicast. The proposed method, to the best of our knowledge, is the first JFE method in the TSH transform domain using social network analysis. The use of fingerprinting along with encryption can provide a double-layer of protection to media sharing in social network environment. Theory analysis and experimental results show the effectiveness of the proposed JFE scheme.