Identity based proxy re-encryption scheme (IBPRE+) for secure cloud data sharing

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In proxy re-encryption (PRE), a proxy with re-encryption keys can transfer aciphertext computed under Alice's public key into a new one, which can be decrypted by Bob only with his secret key. Recently, Wang et al. introduced the concept of PRE plus (PRE+) scheme, which can be seen as the dual of PRE, and is almost the same as PRE scheme except that the re-encryption keys are generated by the encrypter. Compared to PRE, PRE+ scheme can easily achieve two important properties: first, the message-level based fine-grained delegation and, second, the non-transferable property. In this paper, we extend the concept of PRE+ to the identity based setting. We propose a concrete IBPRE+ scheme based on 3-linear map and roughly discuss its properties. We also demonstrate potential application of this new primitive to secure cloud data sharing.Peer ReviewedPostprint (author's final draft

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, we apply the m-RNN model to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval. The project page of this work is: www.stat.ucla.edu/~junhua.mao/m-RNN.html .Comment: Add a simple strategy to boost the performance of image captioning task significantly. More details are shown in Section 8 of the paper. The code and related data are available at https://github.com/mjhucla/mRNN-CR ;. arXiv admin note: substantial text overlap with arXiv:1410.109