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Über dieses Buch

This two volume set LNCS 10039 and LNCS 10040 constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Cloud Computing and Security, ICCCS 2016, held in Nanjing, China, during July 29-31, 2016.

The 97 papers of these volumes were carefully reviewed and selected from 272 submissions. The papers are organized in topical sections such as: Information Hiding, Cloud Computing, Cloud Security, IOT Applications, Multimedia Applications, Multimedia Security and Forensics.

Inhaltsverzeichnis

Frontmatter

Information Hiding

Frontmatter

A Blind Image Watermarking Algorithm in the Combine Domain

This paper presents a novel blind digital image watermarking algorithm in the combine domains to resolve the protecting copyright problem. For embedding watermark, the generation principle and distribution features of direct current (DC) coefficient are utilized to directly modify the pixel values in the spatial domain, then 4 different sub-watermarks are embedded into different areas of the host image for four times, respectively. When extracting watermark, the sub-watermark is extracted with blind manner according to DC coefficients of watermarked image and the key-based quantization step, and then the statistical rule and “first to select, second to combine” are proposed to form the final watermark. Hence, the proposed algorithm not only has the simple and quick performance of the spatial domain but also has the high robustness feature of DCT domain. Many experiments have proved the proposed watermarking algorithm has good invisibility of watermark and strong robustness for many added attacks, e.g., JPEG compression, cropping, adding noise, etc. Comparison results also have shown the preponderances of the proposed algorithm.

Qingtang Su

Reversible Contrast Enhancement

This paper proposes a novel idea of reversible contrast enhancement (RCE) for digital images. Different from the traditional methods, we aim to embed the reversible feature into image contrast enhancement, making sure that the processed image can be losslessly turned back to the original. The original image is enhanced by histogram shrink and contrast stretching. Meanwhile, side information is generated and then embedded into the contrast enhanced image. On the other end, we extract side information from the processed image and reconstruct the original content without any error. Experimental results show that good contrast and good quality can be achieved in the RCE processed image.

Zhenxing Qian, Xinpeng Zhang, Weiming Zhang, Yimin Wang

On Improving Homomorphic Encryption-Based Reversible Data Hiding

Reversible data hiding for encrypted images with improved performance is introduced in this paper. Each unit in the original image is separated into three components, and each component is encrypted by Paillier homomorphic encryption. Additional bits can be concealed into the encrypted image by manipulating the encrypted signals. Finally, the original image is obtained without error when the direct decryption is applied. The embedded bits are perfectly extracted as well. Optimal visual quality and improved embedding rate are obtained by the proposed approach, since the value of the directly decrypted unit is the same as the original one. Experimental results and comparisons are demonstrated to illustrate the effectiveness and advantages of the proposed method.

Xiaotian Wu, Zhuoqian Liang, Bing Chen, Tong Liu

Coverless Information Hiding Method Based on Multi-keywords

As a new information hiding method, coverless information hiding has become a hot issue in the field of information security. The existing coverless information hiding method has realized that one Chinese character can be hidden in one natural text. However, the problem of the method is that the hiding capacity is too small. To address this problem, a novel method named coverless information hiding method based on multi-keywords is proposed in this paper. The main idea of the method is that the number of keywords will be hidden in the text where keywords have been hidden. Experimental results show that the proposed method can improve the capacity of information hiding in text.

Zhili Zhou, Yan Mu, Ningsheng Zhao, Q. M. Jonathan Wu, Ching-Nung Yang

Reversible Data Hiding with Low Bit-Rate Growth in H.264/AVC Compressed Video by Adaptive Hybrid Coding

For the reversible data hiding algorithm in compressed-domain videos, secret data embedding process usually lead to a large bit-rate growth of video stream, thus the imperceptibility and security will be decreased. In order to solve this problem, a reversible data hiding algorithm with low bit-rate growth in H.264/AVC compressed video by adaptive hybrid coding is proposed in this paper. In the proposed scheme, the hybrid coding algorithm has been adaptively applied on different trailing coefficients, which makes the algorithm have a considerable embedding capacity and a low bit-rate growth. Compared with the existing reversible data hiding algorithm for video, the proposed scheme has a lower bit-rate growth with the same hiding capacity.

Tian-Qi Wang, Hong-Xia Wang, Yue Li

An Information Hiding Algorithm for HEVC Based on Differences of Intra Prediction Modes

Concerning the problem that the existing HEVC video data hiding algorithms have great influence on the video quality, a novel algorithm based on differences of intra prediction modes was proposed. During the process of 4 × 4 luminance prediction coding, we establish a mapping relationship between secret bits and differences of two consecutive intra predictions with directions and embed secret bits by modulating the prediction modes according to Lagrange rate distortion model. For two consecutive non-directional prediction modes, i.e. planar mode or DC mode, we embed the secret bits by modifying the prediction modes directly in order to increase the embedding capacity. The extraction of secret bits only requires decoding the intra prediction modes from the bit stream. The experimental results show that the average decrease of peak signal to noise ratio (PSNR) value was about 0.05 dB. Increment of bitrate was bounded to 1.1 %. The structural similarity (SSIM) values are all about 0.95. The experimental results indicate that the proposed algorithm has little impact on the video quality and also has considerable capacity.

Qi Sheng, Rangding Wang, Anshan Pei, Bin Wang

Improvement of Universal Steganalysis Based on SPAM and Feature Optimization

The tendency for high-dimension of universal steganalysis characteristics toward intensifying, and lead to the rapid rise in complexity of algorithm in time and space domain. So maintain the level of detection rates, and reduce the dimension of features at the same time, have significance in research of steganalysis. This paper determines the optimal dimension of feature vectors by principal component analysis; using the concept of Fisher linear discriminant, with the degree of “aggregations within class” and “discreteness between classes” to evaluate the ability of each dimension features to distinguish natural and hidden carrier, and then select the optimal subset. The analysis directs at the mainstream universal steganalysis model–SPAM model, and the simulation results show that optimal subset has a good detection and low computational complexity.

Lei Min, LiuXiao Ming, Yang Xue, Yang Yu, Wang Mian

Optimizing Feature for JPEG Steganalysis via Gabor Filter and Co-occurrences Matrices

For modern steganography algorithms, there are many distortion functions designed for JPEG images which are difficult to be detected for the steganalyst. Until now, the most successful detection of this kind steganography named GFR (Gabor Filter Residual) is currently achieved with detectors for training on cover and stego sets. These features extract the image texture information from different scales and orientations, and the image statistical characteristics can be captured more effectively. In this paper, we describe a novel feature set for steganalysis of JPEG images. The features are composed of two parts. All of them are obtained based on GFR in the spatial domain. Its first part is to extract the histograms features, and the other part is co-occurrence matrices features. Due to its high dimensionality, we make the best of the label to reduce these features. Compared with state-of-the-arts methods, the most advantage of this proposed steganalysis features is its lower detection error while meeting the advanced steganographic algorithms.

Bing Cao, Guorui Feng, Zhaoxia Yin

Improved Separable Reversible Data Hiding in Encrypted Image Based on Neighborhood Prediction

Recently, separable reversible data hiding in encrypted image attracts more and more attention. Data extraction and image decryption are separable in the separable reversible data hiding method in encrypted image, which makes it possible for cloud server to extract the additional data without knowing the original content. In this paper, we focus on the user side and introduced two improved methods based on neighborhood prediction to obtain the good quality decrypted image without data hiding key. Our experiment results prove both the two methods achieve good performance on decrypted image.

Shu Yan, Fan Chen, Hongjie He

Fragile Watermarking with Self-recovery Capability via Absolute Moment Block Truncation Coding

In this paper, we propose a fragile image watermarking scheme based on Absolute Moment Block Truncation Coding (AMBTC) and self-embedding. According to the constructed binary map and two reconstruction levels, each non-overlapping block in original image can be compressed with the AMBTC algorithm. Then, after scrambling, the compression codes are extended through a random matrix, which can introduce more redundancy into the reference bits to be embedded for content recovery. Also, the relationship between each image block and each reference bit is built so that the recoverable area for tampered image can be increased. Experimental results demonstrate the effectiveness of the proposed scheme.

Ping Ji, Chuan Qin, Zhenjun Tang

Schur Decomposition Based Robust Watermarking Algorithm in Contourlet Domain

Most of the existing watermarking schemes utilized SVD decomposition to embed watermark, which lead to a high computational complexity and incidental the false positive detection problem. Therefore, this paper provides a robust copyright protection scheme based on a simple decomposition scheme (schur decomposition) and Quantization Index Modulation (QIM) in Contourlet domain. In addition, some stable features are acquired by using schur decomposition in the Contourlet domain. Consequently, the watermark is embedded into those stable features with QIM method. Experimental results show that the proposed scheme has some superiorities in terms of robustness and imperceptibility, which could against most common attacks such as JPEG compression, filtering, cropping, noise adding and so on.

Junxiang Wang, Ying Liu

Improved Uniform Embedding for Efficient JPEG Steganography

With the wide application of the minimal distortion embedding framework, a well-designed distortion function is of vital importance. In this paper, we propose an improved distortion function for the generalized uniform embedding strategy, called improved UERD (IUERD). Although the UERD has made great success, there still exists room for improvement in designing the distortion function. As a result, the mutual correlations among DCT blocks are utilized more efficiently in the proposed distortion function, which leads to less statistical detectability. The effectiveness of the proposed IUERD is verified with the state-of-the-art steganalyzer - JRM on the BOSSbase database. Compared with prior arts, the proposed scheme gains favorable performance in terms of secure embedding capacity against steganalysis.

Yuanfeng Pan, Jiangqun Ni, Wenkang Su

A Tunable Bound of the Embedding Level for Reversible Data Hiding with Contrast Enhancement

Recently, histogram modification based reversible data hiding (RDH) techniques are exploited to enhance the image contrast. To avoid overflow and underflow, the cover image has to be pre-processed by pre-shifting a number of the histogram bins at the lower and upper ends. When the payload size becomes large, a larger number of histogram bins has to be pre-shifted, and thus the image contrast may be over enhanced. As a result, human eye perceivable image degradation may appear in the over-sharpened image. To avoid image over-sharping, the just noticeable difference measurement is exploited to estimate the maximum number of pre-shiftable histogram bins. In addition, a tunable parameter is designed to balance between the visual degradation and the embedding capacity. The experimental result shows that the proposed work is effective in estimating the bound of embedding level.

Haishan Chen, Wien Hong, Jiangqun Ni, Junying Yuan

Coverless Text Information Hiding Method Based on the Word Rank Map

Text information hiding has attracted the attention of many scholars and made a lot of achievements. However, for the text information hiding methods such as text format-based, generation method-based, text image-based and so on, there is a major flaw that cannot resist steganography detection. Based on the rank map of the words, a novel method of coverless text information hiding is presented. Firstly, stego-vectors are directly generated from the secret message by using the rank map. Then, some normal texts, stego-texts including the generated sgeto-vectors, will be searched form the text big data. Finally, the secret information can be sent to the receiver without any modification of the stego-texts. The proposed algorithm has a higher theoretical significance and practice value because it is robust for almost all current steganalysis methods.

Jianjun Zhang, Jun Shen, Lucai Wang, Haijun Lin

A Phishing Webpage Detecting Algorithm Using Webpage Noise and N-Gram

Although anti-phishing solutions were highly publicized, phishing attack has been still an important serious problem. In this paper, a novel phishing webpage detecting algorithm using the webpage noise and n-gram was proposed. Firstly, the phishing webpage detecting algorithm extracts the webpage noise from suspicious websites, and then expresses it as a feature vector by using n-gram. Lastly, the similarity of feature vector between the protected website and suspicious is calculated. Experimental results on detecting phishing sites samples data show that: this algorithm is more effective, accurate and quick than existing algorithms to detect whether a site is a phishing website.

Qiong Deng, Huajun Huang, Liangmin Pan, Shuang Pang, Jiaohua Qin

A Construction Scheme of Steganographic Codes Based on Matrix Unwrapping

As the most efficient matrix embedding scheme, syndrome-trellis codes (STCs) has been widely used in the field of data hiding, it is implemented based on syndrome trellis structure of convolutional codes and the Viterbi algorithm. In this paper, a new construction scheme of STCs is proposed based on a family of time-varying periodic convolutional codes, the parity-check matrix is constructed by matrix unwarpping. The proposed scheme can enhance the parameter recognition of STCs efficiently while maintaining similar performance with STCs. Moreover, their construction method is more systematic.

Weiwei Liu, Guangjie Liu, Jiangtao Zhai, Yuewei Dai

A Technique of High Embedding Rate Text Steganography Based on Whole Poetry of Song Dynasty

Text steganography is a kind of technique which can transmit secret information using text as carrier. Compared with other types of carriers such as image and video, the embedding rate and capacity of text steganography algorithm is generally lower due to its low redundancy. A text steganography algorithm using single Ci-Pai of song poetry as a template and the embedding rate of it reaches 16.1 %. This paper presents a new text steganography algorithm based on song poetry with a higher embedding rate 27.1 %. In our proposed scheme, by selecting and processing 2538 pieces of song poetry which from 145 pieces of different Ci-Pai, a system which includes the template, the large capacity of lexicon, the encoder and the decoder is constructed. According to users’ needs, the proposed scheme can generate arbitrary Ci-Pai to make the secret messages hidden in the song poetry which metrical patterns, rhyme words completely accord with the Ci-Pai. Additionally, we confirm our high embedding rate through theoretical analysis and extensive experiments.

Yanchen Liu, Jian Wang, Zhibin Wang, Qifeng Qu, Shun Yu

Cloud Computing

Frontmatter

Multi-objective Ant Colony Optimization Algorithm Based on Load Balance

Virtual machine (VM) placement is a process of mapping VMs to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony optimization algorithm based on load balance (MACOLB) for the VM placement problem. Firstly, the algorithm for a multi-objective context is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. Secondly, the pheromone adjustment factor (PAF) is given according to the load of physical machine (PM) and the pheromone update rule is transformed correspondingly. Finally, the effectiveness of the proposed algorithm is evaluated by the simulation.

Liwen Zhu, Ruichun Tang, Ye Tao, Meiling Ren, Lulu Xue

A Novel Spatio-Temporal Data Storage and Index Method for ARM-Based Hadoop Server

During the past decade, a vast number of GPS devices have produced massive amounts of data containing both time and spatial information. This poses a great challenge for traditional spatial databases. With the development of distributed cloud computing, many high-performance cloud platforms have been built, which can be used to process such spatio-temporal data. In this research, to store and process data in an effective and green way, we propose the following solutions: firstly, we build a Hadoop cloud computing platform using Cubieboards2, an ARM development board with A20 processors; secondly, we design two types of indexes for different types of spatio-temporal data at the HDFS level. We use a specific partitioning strategy to divide data in order to ensure load balancing and efficient range query. To improve the efficiency of disk utilisation and network transmission, we also optimise the storage structure. The experimental results show that our cloud platform is highly scalable, and the two types of indexes are effective for spatio-temporal data storage optimisation and they can help achieve high retrieval efficiency.

Laipeng Han, Lan Huang, Xueyi Yang, Wei Pang, Kangping Wang

An Efficient Hierarchical Comparison Mechanism for Cloud-Based Heterogeneous Product Lifecycle Management Systems

Cloud computing has enabled various product lifecycle management (PLM) systems from different parts venders to form a heterogeneous system to facilitate joint product development. To avoid the performance issue caused by traversal multiple different PLM systems in hierarchical comparison, in this research we have developed an efficient hierarchical comparison mechanism for cloud-based heterogeneous PLM systems. We have used this mechanism to convert the traditional hierarchical comparison by traversal through the tree type data structure into a string comparison. Our approach is very simple but scalable, and also very suited to the cloud-based heterogeneous PLM system because the method itself is independent to the details of data stores. Therefore we have offered an efficient solution for a specific interoperability problem in cloud-based heterogeneous product lifecycle management systems.

Mikayla Cohen, Yanzhen Qu

Automatic Classification of Cloud Service Based on Weighted Euclidean Distance

Because the wide use of cloud computing has led to vast amounts of service information in the network, the quick identification and the automatic classification of cloud services have become the key to the quick and accurate location of the expected service.In this paper, a new method of automatic classification of cloud services based on weighted Euclidean distance is proposed. Firstly, we perform the service pretreatment on the collected dataset, and extract features to build a text model based on VSM (Vector Space Model). Further on, a new algorithm named WT_K-means algorithm is proposed by improving the distance function of the original K-means algorithm. Experiments have been carried out under two-dimensional test set and real service dataset, respectively. The results show that the pretreatment of service can abstract the functional characteristics of the original WSDL (Web Service Description Language) documents, and the proposed WT_K-means algorithm can effectively classify services according to these characteristics.

Yanqiu Lou, Yi Zhuang, Ying Huo

A Conflict Prevention Scheduling Strategy for Shared-State Scheduling in Large Scale Cluster

The scheduling strategy is being challenged by the scaling cloud size and more complicated application requirements. Omega provides a share state scheduling architecture to achieve flexible and scalable performance. However, there is few studies aim at the scheduling strategy for shared-state scheduling architecture. So it is worthy further research. In this paper, we present a conflict prevention scheduling strategy for shared-state scheduling architecture. Conflict prevention scheduling strategy gives a feasible solution to reduce conflict and improve efficient for parallel schedulers in shared-state scheduling architecture. We implement it in Omega’s public simulator, experiments results show that conflict prevention scheduling strategy is effective and can significantly improves the efficiency of scheduler with long decision time.

Libo He, Zhenping Qiang, Lin Liu, Wei Zhou, Shaowen Yao

Design and Performance Comparison of Modular Multipliers Implemented on FPGA Platform

Modular multiplier is the most critical component in many data security protocols based on public key cryptography (PKC). To provide data security in many real time applications, a high performance modular multiplier is of utmost importance. Two techniques mostly used for high speed modular multiplication are Montgomery Modular Multiplication (MMM) and Interleaved Modular Multiplication (IMM). This paper presents radix-2 hardware implementation of the MMM and IMM methods with detailed performance analysis. The designs are implemented in Verilog HDL and synthesized targeting Xilinx Virtex-6 FPGA platform. Synthesized results indicate that the radix-2 MMM design is better in terms of computation time, FPGA slice area and throughput as compared to the radix-2 IMM design.

Khalid Javeed, Daniel Irwin, Xiaojun Wang

Cloud-Based Video Surveillance System Using EFD-GMM for Object Detection

Nowadays, new generation of video surveillance systems integrates lots of heterogeneous cameras to collect, process, and analyze video for detecting the objects of potential security threats. The existing systems tend to reach the limit in terms of scalability, data access anywhere, video processing overhead, and massive storage requirements. A novel cloud computing can provide scalable and powerful techniques for large-scale storage, processing, and dissemination of video data. Furthermore, the integration of cloud computing and video processing technology offers more possibilities for efficient deployment of surveillance systems. This paper deploys the framework of a cloud-based video surveillance system and proposes an EFD-GMM approach for object detection in the overhead video processing. A prototype surveillance system is also designed to validate the proposed approach. It finally shows that the proposed approach is more efficient than GMM in video processing of cloud-based system.

Ce Li, Jianchen Su, Baochang Zhang

Enhanced Edge Detection Technique for Satellite Images

Traditional Canny edge detection algorithm is sensitive to noise, therefore when filtering out this noise weak edge information gets lose easily. In response of these problems an improved canny edge detection algorithm was proposed by Weibin Rong, Zhanjing Li, Wei Zhang and Lining Sun. The improved canny algorithm introduces the concept of gravitational field intensity to obtain the gravitational field intensity operator while replacing image gradients. Based on standard deviation and the mean of image gradient magnitude were put forward for two kinds of typical image among which one has the rich edge information and another has relatively poor edge information. The experimental results says that algorithm preserve more edge information but it’s computing speed was relatively slow. In response of these problem this paper proposes an Enhanced edge detection algorithm which uses the concept of double derivative Gaussian filter and is much faster than the improved canny algorithm. The Experimental Analysis has been done based on time, peak-signal to noise-ratio (PSNR) and entropy which states that algorithm preserves more edge information and is more robust to noise.

Renu Gupta

Phone Call Detection Based on Smartphone Sensor Data

Smartphones are now equipped with as many as 30 embedded sensors, which have been widely used in human activity recognition, context monitoring, and localization. In this paper, we propose a phone call detection scheme using smartphone sensor data. We design Android applications to record, upload and display smartphone sensor data. We show how proximity and orientation sensors together can be used to accurately predict phone calls. Furthermore, the activity state during a phone call can be classified into three categories: sitting/standing, lying down, and walking. Features are extracted from proximity and orientation sensors to determine the range of values satisfying each state. Our system achieves an overall accuracy of 85 %.

Huiyu Sun, Suzanne McIntosh

A Survey of Speculative Execution Strategy in MapReduce

MapReduce is a parallel computing programming model designed to process large-scale data. Therefore, the accuracy and efficiency for computing are needed to be assured and speculative execution is an efficient method for calculation of fault tolerance. It reaches the goals of shortening the execution time and increasing the cluster throughput through selecting slow tasks and speculative copy these tasks on a fast machine to be executed. Hadoop naïve speculative execution strategy assumes that the cluster is homogeneous, and this assumption leads to the poor performance in heterogeneous environment. Several speculative execution strategies which aim to improve the MapReduce Performance in the heterogeneous environments are reviewed in this paper like LATE, MCP, ex-MCP and ERUL, then the comparison between these methods are listed.

Qi Liu, Dandan Jin, Xiaodong Liu, Nigel Linge

Cloud Security

Frontmatter

Cryptanalysis and Improvement of a Smart Card Based Mutual Authentication Scheme in Cloud Computing

Cloud computing enables the users to access and share the data as and when required at anytime from anywhere. Due to its open access, one of the major issues faced by cloud computing is how to prevent the outsourced data from being leaked to unauthorized users. Therefore, mutual authentication between the user and the cloud service provider is a necessity to ensure that sensitive data in the cloud are not available to illegal users. Recently, Li et al. proposed a two-factor authentication protocol based on elliptic curve cryptosystem which enables the cloud users to access their outsourced data. However, we first show that their scheme suffers from the problem of wrong password login. Secondly, their scheme is prone to denial of service attack in the password-changing phase. Thirdly, it fails to provide user revocation when the smart card is lost or stolen. To remedy these flaws, we propose an improved two-factor authentication and key agreement protocol, which not only guards various known attacks, but also provides more desired security properties.

Qi Jiang, Bingyan Li, Jianfeng Ma, Youliang Tian, Yuanyuan Yang

Another SPA Key Recovery Against Random Order Countermeasures for AES Key Expansion

To increase the resistance against power analysis, random order countermeasure applied to AES key expansion was proposed and evaluated by Clavier et al. in CHES 2014. The proposed column-wise random order countermeasure showed certain resistance when the power consumption of the key expansion part is used for key recovery. For further evaluation, Clavier et al. analyzed the improvement of key recovery attack using fault injection as additional information. As for the acceleration of the key recovery, this work argues that extracting power information of AES state is more preferred than performing fault injections for practical attackers. This work comprehensively evaluates the random order countermeasure assuming the attackers use the power consumptions of AES state to accelerate the key recovery. We studied the relationship between key recovery result and the amount of information from AES state via both theoretical analysis and key recovery simulations. The results (a) demonstrate a set of effective key extractions with no fault injections and (b) discover the most cost-effective attack is extracting Hamming weight of 12 bytes for 2 AES executions, whose key extraction averagely finishes in 1 min.

Mengting Chen, Yang Li, Jian Wang

A Note on “IPad: ID-Based Public Auditing for the Outsourced Data in the Standard Model”

Cloud storage is an increasingly popular data storage manner which allows cloud data owners to outsource their data to the cloud for storage and maintaining. However, users will lose their physical control over their data after their data are outsourced to the cloud. To ensure the integrity of data stored in the cloud, many public auditing schemes have been proposed. Recently, Zhang et al. proposed an ID-based public auditing scheme for the outsourced data in the standard model. In this note, we prove this scheme is not secure. We show that the malicious cloud can pass the auditor’s verification even if it has deleted or modified the users’ data in this scheme.

Wenting Shen, Jia Yu, Hui Xia, Rong Hao

Supervised Nonlinear Latent Feature Extraction and Regularized Random Weights Neural Network Modeling for Intrusion Detection System

Colinearity and latent relation among different input features of net work intrusion detection system (IDS) have to be addressed. The strong nonlinearity and uncertain mapping between input features and network intrusion behaviors lead to difficulty to built effective detection model for IDS. In this paper, a new supervised nonlinear latent feature extraction and fast machine learning algorithm based on global optimization strategy is proposed to solve these problems. Specifically, for diminishing colinearity among input variables, kernel partial least squares (KPLS) algorithm is employed to extract nonlinear latent features. Then, regularized random weights neural networks (RRWNN) is utilized to construct the intrusion detection model. To optimize the proposed system, the modeling parameters of KPLS and RRWNN are selected in terms of global optimization. Experiments on KDD99 data show that the proposed approach is effective.

Jian Tang, Liu Zhuo, Meiying Jia, Chunlai Sun, Chaowen Shi

A Revocable Certificateless Signature Scheme Without Pairing

In a public key cryptosystem, an important problem is how to revoke a user. As we know, the certificateless public key cryptography (CLPKC) unites the qualities of the traditional public key system and the identity-based public key system. It is free of complicated certificate management and key escrow. However, there are few solutions to the revocation problem in CLPKC. In this paper, we present an efficient revocable certificateless signature scheme. Moreover, this new scheme is free of bilinear pairing. Under the assumption of Discrete Logarithm problem, our scheme is provably secure.

Yinxia Sun, Zhuoran Zhang, Limin Shen

Privacy Protection of Digital Speech Based on Homomorphic Encryption

This paper presents a digital speech encryption scheme based on homomorphic encryption, which uses a symmetrical key cryptosystem (MORE-method) with probabilistic statistics and fully homomorphic properties to encrypt speech signals. In the proposed scheme, each sample of speech signal is firstly multiplied one weight, and then encrypted, the normalization is exploited to make the data expend lossy compression. Finally, a recombination method of the cipher-text is proposed to obtain the corresponding speech cipher-text with good performances. Experimental results show that the proposed scheme is homomorphism, which has strong diffusibility and a large key-space. What’s more, it is robustness to statistical analysis attacks, decreased the residual intelligibility as small as possible. Moreover, the encrypted speech can be decrypted completely. Compared with two dimensional chaotic and Paillier cryptosystem, the proposed scheme is more security and lower complexity, so the proposed scheme especially meets the sensitive speech security in the cloud.

Canghong Shi, Hongxia Wang, Qing Qian, Huan Wang

Two Factor Authenticated Key Exchange Protocol for Wireless Sensor Networks: Formal Model and Secure Construction

Two-factor authenticated key exchange (TFAKE) protocols are critical tools for ensuring identity authentication and secure data transmission in wireless sensor networks (WSNs). Until now, numerous TFAKE protocols based on smart cards and passwords are proposed for WSNs. Unfortunately, most of them are found insecure against various attacks. Researchers focus on cryptanalysis of these protocols and then fixing the loopholes. Little attention has been paid to design rationales and formal security models of these protocols. In this paper, we first put forward a formal security model for TFAKE protocols in WSNs. We then present an efficient TFAKE protocol for WSNs without using expensive asymmetric cryptology mechanisms. Our protocol can be proven secure in the random oracle model and achieves user anonymity. Compared with other TFAKE protocols, our protocol is more efficient and enjoys provable security.

Fushan Wei, Ruijie Zhang, Chuangui Ma

Outsourced Data Modification Algorithm with Assistance of Multi-assistants in Cloud Computing

The rapid development of cloud storage in these years has caused a wave of research craze. To improve the cloud user experience, a large amount of schemes are proposed with various practical performances, for instance, long term correct data storage and dynamic data modification. In most works, however, the authors seem to completely ignore the hard fact that data owner alone could not have enough energy to discover and correct all the inappropriate data outsourced in cloud. Others did consider it, and gave more than one user both read and write permissions, which leads to chaotic management of multiusers. In this paper, we propose a novel algorithm, in which the data owner and several authenticated assistants form a team to support dynamic data modification together. Assistants are in charge of detecting problems in cloud data and discussing a corresponding modification suggestion, while data owner is responsible for the implementation of the modification. In addition, our algorithm supports identity authentication, efficient malicious assistant revocation, as well as lazy update. Sufficient numerical analysis validates the performance of our algorithm.

Jian Shen, Jun Shen, Xiong Li, FuShan Wei, JiGuo Li

Location Privacy Protected Recommendation System in Mobile Cloud

As the core of location-based services (LBS), the LBS-oriented recommendation systems, which suggest the points-of-interest (POIs) to users by analyzing the distribution of the user’s previous points-of-interest, have attracted great interest from both academia and industry. Despite the convenience brought by the LBS-oriented recommendation systems, most of current systems require users to expose their locations, which give rise to a big concerning of the location privacy issues. Meanwhile, as the defacto LBS infrastructure, the mobile-cloud computing paradigm introduces new opportunities and challenges to solve the privacy issues in LBS-oriented recommendation systems. To this end, we propose a novel location-privacy protected scheme for mobile-cloud based recommendation system. The scheme consists of two parts. (1) The server analyzes the user behavior pattern and then makes a list of sketchy recommendation, named as the recommended candidate list. (2) Mobile phone downloads the recommended candidate list from the server and refines the recommendation by taking the current geographical position, current time and location popularity into consideration. With the result from real data driven simulations, the scheme is proved to solve the problem of location privacy risks and improve the accuracy of recommendation.

Haiyan Guan, Hongyan Qian, Yanchao Zhao

An Extended Chaotic Maps Based Authenticated Key Agreement Protocol Without Using Password

Chaotic maps have been used in the design of cryptosystem due to its excellent properties. Recently, researchers have proposed many authenticated key agreement protocols based on the chaotic maps. However, most of those protocols use the password to achieve the key agreement, and it will lead some security problems. First, the server has to store a sensitive verification table, and it is dangerous if the server has been compromised or the verification table was stolen. Besides, the low entropy passwords are vulnerable to some password related attacks, such as insider attack and password guessing attack. To resolve the aforementioned problems, this paper propose an extended chaotic maps based authenticated key agreement protocol without using password, where the server just needs to maintain a master secret key and the user just needs to hold a secret key, then they can achieve the key agreement. Compared with other related protocols, the proposed protocol not only keeps the efficiency, but also enhances the security. So, it is more suitable for client/server environment.

Xiong Li, Junguo Liao, Wei Liang, Jingqiang Zhao

A Privacy-Preserving Online Reverse Multi-attributes Auction Scheme Based on Degree-Matching

In recent years, online auction system obtains the widespread application with the vigorous development of e-commerce. During the process of an auction, numbers of qualified suppliers propose their own bidding according to procurements demands. Then the winner is generated by comparing and sorting all of degree-matching between procurers ideal solution and suppliers’ bidding, we remind it as Ideal Degree-Matching Determined Solution (IDDS). IDDS requires suppliers to provide their private information to the auction servers. However, suppliers usually do not expect the real information of the bid leaked out, especially known by other competitors. In this paper we propose a Privacy-Preserving Online Reverse Multi-Attributes Auction Scheme based on Degree-Matching (PRMA). IDDS is used as the basis of determining the auction winner. Impressively based on the difficulty of integer factorization assumption, our scheme ensures data security of all suppliers. Compared with previous work, our scheme also gains higher security performance by eliminating the participation of third party.

Mingfan Ma, Jun Gao, Ning Lu, Wenbo Shi

TransPro: Mandatory Sensitive Information Protection Based on Virtualization and Encryption

With the growing population of networked devices, the potential risk of leaking sensitive data has been seriously increased. This paper proposes a novel approach named TransPro based on virtualization technology, which can provide mandatory protected transmission between different network hosts. Through TransPro, all output sensitive data is encrypted before sent to network, and all input network data is decrypted before handled by the sensitive application. TransPro works in the host OS and VMM, and it does not need to manually modify application code. We have evaluated TransPro using security analysis and attack tests. The results show that TransPro can offer a safe information transmission with a little overhead.

Xue-Zhi Xie, Hu-Qiu Liu, Yu-Ping Wang

Key Recovery in Public Clouds: A Survey on Cross-VM Side Channel Attacks

Isolation across virtual machines is one of the pillars on which the cloud computing paradigm relies on, allowing efficient use of shared resources among users who experience dedicated services. However side channel attacks have been recently demonstrated possible, showing how an adversary is enabled to recover sensible information by observing the behavior of a VM co-located on the same physical machine. In this paper we survey the current attacks, focusing on the ones targeted to extract private RSA keys, and discuss some possible countermeasures, offering a picture of the security challenges cloud providers need to address in order to provide strong guarantees to their customers.

Stelvio Cimato, Ernesto Damiani, Silvia Mella, Ching-Nung Yang

An Outsourcing Data Storage Scheme Supporting Privacy Preserving and Data Hiding Based on Digital Watermarking

Outsourcing data storage systems reduce storage costs of IT Enterprises and maintenance for users, which have attracted much attention. It is an acceptable way to use cryptography technologies to ensure privacy preserving and access control in secure outsourcing data storage scheme. In this paper, we propose an outsourcing data storage scheme which combines digital watermarking and cryptography technology to support privacy preserving and data hiding. We use the multi-granularity encryption algorithm to preserve the privacy of outsourcing data. The RSA-based proxy re-encryption (PRE) algorithm is used to make the key transportation safe. And the decrypted data containing hiding data is approximate to the original data. Experiments show that our scheme is secure and feasible.

Zhangjie Fu, Xinyue Cao

Distributed Quantum Computation Assisted by Remote Toffoli Gate

Distributed quantum computation requires quantum operations to act on logical qubits over a distance. We will develop a formal model for the telegate-based distributive quantum computation. We show that a controlled-controlled-NOT (Toffoli) gate as an elementary gate of the universal quantum computation may be remotely implemented by exploring a high-level quantum system. These remote Toffoli gates cost at most two Einstein-Podolsky-Rosen (EPR) pairs, whereas four or six EPR pairs are required from the teleportation-based quantum computation or the remote CNOT gate, respectively. Thus, the previous Toffoli gate-based circuit synthesis may be used as an elementary subroutine of this distributed quantum computation.

Ming-Xing Luo, Hui-Ran Li

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