Drops: Division And Replication Of Data In Cloud For Optimal Performance & Sec - Coimbatore

Abstract
We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including 2D videos, 3D videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of 3D videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of 3D videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 3D videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of 3D videos, while our system detects more than 98% of them. This comparison shows the need for the proposed 3D signature method, since the state-of-the-art commercial system was not able to handle 3D videos.

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