To resist mobile attacks, a new dynamic threshold signature scheme based on Chinese Remainder Theorem (CRT) was proposed. Firstly, members exchanged their shadows to generate their private keys and the group public key. Secondly, a partial signature was generated by cooperation. Finally, the partial signature was used to synthesize the signature. The scheme does not expose the group private key in the signature process, so that the group private key can be reused. The members update their private keys periodically without changing the group public key to ensure that the signature is still valid before update. Besides, the scheme allows new members to join while keeping the old member's private keys and group private key unexposed. The scheme has forward security, which can resist mobile attacks effectively. Theoretical analysis and simulation results show that, compared with the proactive threshold scheme based on Lagrange interpolation, the updating time consumption of the proposed scheme is constant, therefore the scheme has time efficiency.

Many existing image classification algorithms cannot be used for big image data. A new approach was proposed to accelerate big image classification based on MapReduce. The whole image classification process was reconstructed to fit the MapReduce programming model. First, the Scale Invariant Feature Transform (SIFT) feature was extracted by MapReduce, then it was converted to sparse vector using sparse coding to get the sparse feature of the image. The MapReduce was also used to distributed training of random forest, and on the basis of it, the big image classification was achieved parallel. The MapReduce based algorithm was evaluated on a Hadoop cluster. The experimental results show that the proposed approach can classify images simultaneously on Hadoop cluster with a good speedup rate.