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Bacthing Auditing of Data in Multicloud Storage
Zhihua Xia,Xinhui Wang,Xingming Sun,Yafeng Zhu,Peng Ji,Jin Wang 보안공학연구지원센터 2014 International Journal of Database Theory and Appli Vol.7 No.6
Cloud storage enables users to outsource their data to cloud servers and enjoy the on-demand services. However, this new paradigm also introduces integrity threats toward user’ outsourced data. This paper develops an efficient auditing mechanism, which support batch auditing for multiple data files in multi-cloud environment. By constructing a sequence-enforced Merkle Hash Tree, the proposed protocol can resist the replace attack. By using the bilinear map, the proposed protocol achieves stateless and transparent verification. By putting the computation of intermediate values of the verification on cloud servers, our method can greatly reduce the computing burden of the auditor. The performance analysis proves the good efficiency of the proposed protocol.
Model diagnostics of parametric Tobit model based on cumulative residuals
Sun Zhihua,Guo Yuanyuan,Xie Tianfa,Wang Miaomiao 한국통계학회 2021 Journal of the Korean Statistical Society Vol.50 No.1
In this paper, we investigate the adequate check of the parametric Tobit model. A Cramér–Von Mises type test statistic is constructed, and its asymptotic properties under the null and alternative hypotheses are rigorously studied. The method is efective for the adequacy check of parametric regression models with a scalar or multivariate covariate. Furthermore, it avoids the nonparametric smoothing of the regression function and the choice of the smoothing parameter. Simulation studies are conducted to compare the performance of the proposed test procedure and the existing methods in the literature. A real data set of income is analyzed by applying the proposed method.
Fingerprint Liveness Detection Using Difference Co-occurrence Matrix Based Texture Features
Zhihua Xia,Chengsheng Yuan,Xingming Sun,Rui Lv,Decai Sun,Guangyong Gao 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.11
Fingerprint authentication systems have been widely deployed in both civilian and government applications, however, whether fingerprint authentication systems is security or not has been an important issue under fraudulent attempts through artificial spoof fingerprints. In this paper, inspired by popular feature descriptors such as gray level co-occurrence matrix (GLCM) and Gradient (difference matrix (DM)), we propose a novel software-based fingerprint liveness detection algorithm called difference co-occurrence matrix (DCM). In doing so, quantization operation is firstly conducted on the images. DMs are constructed by calculating difference matrices of horizontal and vertical pixel values of images; difference co-occurrence arrays are constructed from the difference matrices between adjacent pixels. To reduce the influence of abnormal pixel values, truncation is used for DMs. Then, we compute four parameters (Angular Second Moment, Entropy, Inverse Differential Moment and Correlation) used as feature vectors of fingerprint images. For the first time in the fingerprint liveness detection, we construct eight difference co-occurrence matrices and extract texture features from processed DCMs. Finally, SVM classifier is used to predict classification accuracy. The experimental results reveal that our proposed method can achieve more accurate classification compared with the best algorithms of 2013 Fingerprint Liveness Detection Competition, while being able to recognize spoofed fingerprints with a better degree of accuracy.
A Multi-keyword Ranked Search over Encrypted Cloud Data Supporting Semantic Extension
Zhihua Xia,Li Chen,Xingming Sun,Jianxiao Liu 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.8
With the emergence of cloud computing, many data owners outsource their local data to cloud server so as to enjoy high-quality data storage services. For the protection of data privacy, sensitive data has to be encrypted before outsourcing, which makes effective data utilization a challenging task. Although existing searchable encryption technologies enable data users to conduct secure search over encrypted data, the functionality of these schemes need to be further improved. In this paper, we construct a secure and efficient multi-keyword ranked search scheme which supports both the semantic extension search and the multi-keyword ranked search. The semantic extension is achieved through the mutual information statistical analysis of keywords. And the multi-keyword ranked search is achieved through a balanced binary tree whose nodes are the vectors of term frequency (TF) values. The splitting operation and secure transformation are utilized to encrypt the vectors of index and query. Note that, the encrypted vectors can be well used to calculate accurate relevance scores. Phantom terms are added to the index vector to blind the search results to resist statistical attacks. Due to the use of tree-based index structure, the proposed scheme can achieve the sub-linear search time. Finally, the experiments are conducted to demonstrate the efficiency of the proposed scheme.