With the rapid development of information technology, Big data has become a hot topic of research in governments, academia, and enterprises. On the one hand, Big data brings opportunities that are not available by small‐scale data in many fields, such as business, education, and healthcare. On the other hand, due to the huge amount, the development of Big data is still facing many security and privacy issues in the whole lifetime of Big data. Security and privacy issues not only plague users and businesses, but also create obstacles to the expected opportunities and progress of Big data. In this study, we first review challenges in Big data security and privacy, especially considering the challenges in different lifetime of Big data. Then we review four popular secure technologies: homomorphic encryption, secure multiparty computation, attribute‐based encryption, and anonymous protection in social network. Based on the reviews, we identify recommendations and 3 possible directions for security and privacy protection of Big data.
The success of CNNs is accompanied by deep models and heavy storage costs. For compressing CNNs, we propose an efficient and robust pruning approach, cross-entropy pruning (CEP). Given a trained CNN model, connections were divided into groups in a group-wise way according to their corresponding output neurons. All connections with their cross-entropy errors below a grouping threshold were then removed. A sparse model was obtained and the number of parameters in the baseline model significantly reduced. This letter also presents a highest cross-entropy pruning (HCEP) method that keeps a small portion of weights with the highest CEP. This method further improves the accuracy of CEP. To validate CEP, we conducted the experiments on low redundant networks that are hard to compress. For the MNIST data set, CEP achieves an 0.08% accuracy drop required by LeNet-5 benchmark with only 16% of original parameters. Our proposed CEP also reduces approximately 75% of the storage cost of AlexNet on the ILSVRC 2012 data set, increasing the top-1 errorby only 0.4% and top-5 error by only 0.2%. Compared with three existing methods on LeNet-5, our proposed CEP and HCEP perform significantly better than the existing methods in terms of the accuracy and stability. Some computer vision tasks on CNNs such as object detection and style transfer can be computed in a high-performance way using our CEP and HCEP strategies.
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