In order to solve the problems of copyrights infringement, high cost and complex process of rights protection in current media convergence center, a digital rights management system based on blockchain technology and IPFS (Inter Planetary File System) technology is proposed. Considering that large files such as video and audio cannot be stored on the blockchain directly, IPFS technology is adopted as the data expansion scheme for the data storage layer of the Ethereum platform, IPFS protocol is further used for distributed data storage and transmission of media content. In addition, smart contract is also used to uniquely identify digital rights through NFT (Non-fungible Tokens), which provides the characteristics of digital rights transferability and traceability, and realizes an open, transparent, tamper-proof and traceable digital rights management system for media convergence center. Several experimental results show that it has higher transaction success rate, lower storage consumption and transaction confirmation delay than existing scheme.

To realize an information-centric networking, IPFS (InterPlanetary File System) generates a unique ContentID for each content by applying a cryptographic hash to the content itself. Although it could improve the security against attacks such as falsification, it makes difficult to realize a similarity search in the framework of IPFS, since the similarity of contents is not reflected in the proximity of ContentIDs. To overcome this issue, we propose a method to apply a locality sensitive hash (LSH) to feature vectors extracted from contents as the key of indexes stored in IPFS. By conducting experiments with 10,000 random points corresponding to stored contents, we found that more than half of randomly given queries return a non-empty result for the similarity search, and yield an accurate result which is outside the σ confidence interval of an ordinary flooding-based method. Note that such a collection of random points corresponds to the worst case scenario for the proposed scheme since the performance of similarity search could improve when points and queries follow an uneven distribution.