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Spontaneous Reporting System (SRS) has been widely established to collect adverse drug events. Thus, SRS promotes the detection and analysis of ADR (adverse drug reactions), such as the FDA Adverse Event Reporting System (FAERS). The SRS data needs to be provided to researchers. Meanwhile, the SRS data is publicly available to facilitate the study of ADR detection and analysis. In general, SRS data contains private information of some individual characteristics. Before the information is published, it is necessary to anonymize private information in the SRS data to prevent disclosure of individual privacy. There are many privacy protection methods. The most classic method for protecting SRS data is called as PPMS. However, in the real world, SRS data is growing dynamically and needs to be published regularly. In this case, PPMS has some shortcomings in the memory consumption, anonymity efficiency, data update and data security. To remove these shortcomings, we propose an Efficient Q-value Zero-leakage protection Scheme in SRS regularly publishing private data, called EQZS. EQZS can deal with almost all of potential attacks. Meanwhile, EQZS removes the shortcomings of PPMS. The experimental results show that our scheme EQZS solves the problem of privacy leakage in SRS regularly publishing private data. Meanwhile, EQZS significantly outperforms PPMS on the efficiency of memory consumption, privacy anonymity and data update.

The arrival of cloud computing age makes data outsourcing an important and convenient application. More and more individuals and organizations outsource large amounts of graph data to the cloud computing platform (CCP) for the sake of saving cost. As the server on CCP is not completely honest and trustworthy, the outsourcing graph data are usually encrypted before they are sent to CCP. The optimal route finding on graph data is a popular operation which is frequently used in many fields. The optimal route finding with support for semantic search has stronger query capabilities, and a consumer can use similar words of graph vertices as query terms to implement optimal route finding. Due to encrypting the outsourcing graph data before they are sent to CCP, it is not easy for data customers to manipulate and further use the encrypted graph data. In this paper, we present a solution to execute privacy-guarding optimal route finding with support for semantic search on the encrypted graph in the cloud computing scenario (PORF). We designed a scheme by building secure query index to implement optimal route finding with support for semantic search based on searchable encryption idea and stemmer mechanism. We give formal security analysis for our scheme. We also analyze the efficiency of our scheme through the experimental evaluation.

With the popularity of cloud computing, cloud outsourcing has aroused great concern in academia. A lot of graph data are outsourced to the cloud for saving cost. As the cloud server may be not entirely reliable, the outsourced graph data are usually encrypted before sending to the cloud for the security considerations. Adjacency search is a basic operation, and many other operations can be performed based on the adjacency search. Adjacency search supporting synonym query is a more meaningful operation which can make a query user improve the scope of the query. Due to the graph data being encrypted in the cloud, adjacency search supporting synonym query becomes a very challenging task. In this paper, we propose an efficient solution to perform privacy-assured adjacency search supporting synonym query on the encrypted graph in the cloud (PASQ). Our work utilizes a stemming algorithm and an encryption mechanism to perform adjacency search. A query user can get a wider range of search results by this solution, and the privacy of query will not be disclosed. We present a design scheme for the outsourced graph and analyze the security. We demonstrate the efficiency of our scheme by the experiment results on a real graph data.

Recently, cloud computing has drawn much attention from research and industrial communities. With adoption of cloud computing, data providers can reap huge economic benefits by outsourcing their data to cloud. Because of serious privacy concerns, sensitive data should be encrypted before being outsourced. In order to share the encrypted and outsourced data, data providers need to distribute different keys to legal users. Thus, in the scenario of multiple data providers, a legal user may receive lots of keys from different providers and suffer from heavy burden from key management. In this paper, we proposed an identity-based key management scheme, which can let a legal user only maintain a single root key regardless of the number of data providers. Based on the root key, a legal user can derive different resource keys to decrypt ciphertexts from different data providers. With the help of outsourcing decryption technique, a legal user could reduce the decryption overhead by outsourcing some decryption work to cloud. We proofed the correctness and analyzed the security of the proposed scheme, further implemented the proposed scheme, and conducted extensive experiments. The results demonstrate the efficiency and practicality of our scheme.

Integrating deep learning with traditional machine learning methods is an intriguing research direction. For example, PCANet and LDANet adopts Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (LDA) to learn convolutional kernels separately. It is not reasonable to adopt LDA to learn filter kernels in each convolutional layer, local features of images from different classes may be similar, such as background areas. Therefore, it is meaningful to adopt LDA to learn filter kernels only when all the patches carry information from the whole image. However, to our knowledge, there are no existing works that study how to combine PCA and LDA to learn convolutional kernels to achieve the best performance. In this paper, we propose the convolutional coverage theory. Furthermore, we propose the PLDANet model which adopts PCA and LDA reasonably in different convolutional layers based on the coverage theory. The experimental study has shown the effectiveness of the proposed PLDANet model.