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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.
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.
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.
( Zhihua Shen,Yafeng Xia ) 경남대학교 극동문제연구소 2012 ASIAN PERSPECTIVE Vol.36 No.1
Based on newly available Chinese and Russian archival documents and oral histories, this article examines the origins and evolution of Soviet policies concerning China`s nuclear weapons program from 1954 to 1960. The article argues that Soviet premier Nikita Khrushchev consented only to assist China in developing nuclear energy in 1954 only because he needed Mao`s support in a domestic political struggle. But the Taiwan Strait crisis of 1958 unnerved the Russians, leading Khrushchev in June 1959 to rescind his promise to deliver a teaching model A-bomb to the Chinese. By August 1960 all Soviet specialists working on China`s nuclear weapons program were recalled. Nonetheless, the Soviet aid laid the foundation for China`s fledgling nuclear industry.
Fingerprint Liveness Detection Using Gray Level Co-Occurrence Matrix Based Texture Feature
Chengsheng Yuan,Zhihua Xia,Xingming Sun,Decai Sun,Rui Lv 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.10
Fingerprint-based recognition systems have been widely deployed in numerous civilian and government applications. However, the fingerprint recognition systems can be deceived by using an accurate imitation of a real fingerprint such as an artificially made fingerprint. In this paper, we propose a novel software-based fingerprint liveness detection algorithm based on gray level co-occurrence matrix (GLCM), from which we can calculate the texture features of fingerprint images and obtain satisfactory results. For the first time, we extract texture features by constructing four-direction GLCMs in an image, and then quantization operation and normalization operation are adopted. After these, we detected whether a fingerprint image belongs to a real fingerprint or an artificial replica of it. A trained RBF SVM (support vector machine) classifiers scheme is used to make the final live/spoof decision via training and testing feature vectors. The experimental results reveal that our proposed method can discriminate between live fingerprints and fake ones with high classification accuracy.
A Fully Integrated SoC for Smart Capsule Providing In-Body Continuous pH and Temperature Monitoring
Liu, Heng,Jiang, Hanjun,Xia, Jingpei,Chi, Zhexiang,Li, Fule,Wang, Zhihua The Institute of Electronics and Information Engin 2016 Journal of semiconductor technology and science Vol.16 No.5
This paper presents a SoC (System-on-a-Chip) dedicated for a single-chip smart capsule which can be used to continuously monitor human alimentary canal pH and temperature values. The SoC is composed of the pH and temperature sensor interface circuit, a wireless transceiver, the power management circuit and the flow control logic. Fabricated in $0.18{\mu}m$ standard CMOS technology, the SoC occupies a die area of ${\sim}9 mm^2$. The SoC consumes 6.15 mW from a 3 V power supply, guaranteeing the smart capsule battery life is no less than 24 hours when using 50 mAh coin batteries. The experimental results show that measurement accuracy of the smart capsule is ${\pm}0.1$ pH and ${\pm}0.2^{\circ}C$ for pH and temperature sensing, respectively, which meets the requirement of in-body pH and temperature monitoring in clinical practice.
Secure Similarity Search over Encrypted Cloud Images
Yi Zhu,Xingming Sun,Zhihua Xia,Naixue Xiong 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.8
With the growing popularity of cloud computing, more and more data owners are willing to outsource their data to the cloud. However, private data should be encrypted before outsourcing for security requirements, which obsoletes data utilization like content-based image retrieval. In this paper, we propose a secure similarity image search scheme, which allows data owners to outsource their encrypted image database to the cloud server without revealing the real content of images. The proposed scheme supports both global and local feature based image retrieval under various distance metrics, such as earth mover's distance. Firstly, the data owner extracts either global features or local features from images to represent the images. Then, these features are used to generate a searchable index. Finally, both image database and searchable index are encrypted before outsourcing to the cloud server. When a query image coming, the data user extracts feature from the query image and generates the search trapdoor. The trapdoor is then sent to the cloud server and used to compare the similarity with the searchable index. Extensive experiments are conducted to show the efficiency and applicability of our proposed similarity image search system.