Clustering is a commonly used technique for multimedia data analysis and management. In this paper, we propose a high-order CFS algorithm (HOCFS) by extending the traditional CFS algorithm from the vector space to the tensor space for multimedia data clustering. To the improve the efficiency of the high-order CFS algorithm, we propose a privacy preserving high-order CFS algorithm (PPHOCFS) by offloading the expensive computation tasks to the cloud. To protect the private data in the multimedia data sets during the clustering process on the cloud, the proposed model uses the BGV encryption scheme to encrypt the raw data and employs cloud servers to perform the high-order CFS algorithm on the encrypted data efficiently. In our scheme, only the encryption operations and the decryption operations are performed by the client while all the computation tasks are performed on the cloud. Experimental results show that our scheme can securely perform the high-order CFS algorithm for multimedia data clustering on the cloud. More importantly, our scheme is highly scalable to multimedia big data.
With the rapid growth of multimedia messages exchange and digital communication, the multimedia big data has become a research hotspot in various fields. The storage and transmission of multimedia big data have high requirements for security. So how to reinforce multimedia data security has increasingly become an urgent problem. Images, covering highest proportion of multimedia date, should be processed and transmitted with high security. A method for encrypting images of multimedia big database requires high speed and full utilization of the samples since big data has features of huge volume and rapid velocity. It is a favorable property of compressive sampling(CS) for image encryption that the image can be reconstructed from far fewer samples or measurements than traditional methods use. In recent years, CS has been studied not only to reduce the resource requirements for signal acquisition, but also to ensure the security of data. Much work has been done on applying CS in the field of information security. However, it is still an open challenge to improve security and enhance quality of decrypted image simultaneously using the key with small size. In this paper, a CS-based encryption method is presented which associates the quantization with random measurements permutation controlled by scrambled index. In the encryption module, both of the measurement matrix and random change are governed by scrambled indexes, namely the logistic sequence. The quantization is realized by sigmoid function, the parameter of which is regarded as a secret key. In decryption module, the split Bregman iteration algorithm is used to reconstruct the image. An enormous number of experiments have been conducted on both of standard test images and face images chosen from the big database LFW. We also compare this proposal with the existing ones from several perspectives. Experimental results show that our proposal has dramatic improvements on ensuring the security, enhancing the quality of the decrypted image, and raising the efficiency. Additionally, this proposal remarkably reduces storage and transmission resources. Accordingly, this encryption scheme can be applied to ensure the security of multimedia transmission.
Dynamic Adaptive Streaming over HTTP (DASH) is a recently proposed standard that offers different versions of the same media content to adapt the delivery process over the Internet to dynamic bandwidth fluctuations and different user device capabilities. The peer-to-peer (P2P) paradigm for video streaming allows to leverage the cooperation among peers, guaranteeing to serve every video request with increased scalability and reduced cost. We propose to combine these two approaches in a P2P-DASH architecture, exploiting the potentiality of both. The new platform is made of several swarms, and a different DASH representation is streamed within each of them; unlike client-server DASH architectures, where each client autonomously selects which version to download according to current network conditions and to its device resources, we put forth a new rate control strategy implemented at peer site to maintain a good viewing quality to the local user and to simultaneously guarantee the successful operation of the P2P swarms. The effectiveness of the solution is demonstrated through simulation and it indicates that the P2P-DASH platform is able to warrant its users a very good performance, much more satisfying than in a conventional P2P environment where DASH is not employed. Through a comparison with a reference DASH system modeled via the Integer Linear Programming (ILP) approach, the new system is shown to outperform such reference architecture. To further validate the proposal, both in terms of robustness and scalability, system behavior is investigated in the critical condition of a flash crowd, showing that the strong upsurge of new users can be successfully revealed and gradually accommodated.