Reversible Data Hiding in Encrypted Image Based on Block Classification Permutation

Recently, reversible data hiding in encrypted image (RDHEI) has attracted extensive attention, which can be used in secure cloud computing and privacy protection effectively. In this paper, a novel RDHEI scheme based on block classification and permutation is proposed. Content owner first divides original image into non-overlapping blocks and then set a threshold to classify these blocks into smooth and non-smooth blocks respectively. After block classification, content owner utilizes a specific encryption method, including stream cipher encryption and block permutation to protect image content securely. For the encrypted image, data hider embeds additional secret information in the most significant bits (MSB) of the encrypted pixels in smooth blocks and the final marked image can be obtained. At the receiver side, secret data will be extracted correctly with data-hiding key. When receiver only has encryption key, after stream cipher decryption, block scrambling decryption and MSB error prediction with threshold, decrypted image will be achieved. When data hiding key and encryption key are both obtained, receiver can find the smooth and non-smooth blocks correctly and MSB in smooth blocks will be predicted correctly, hence, receiver can recover marked image losslessly. Experimental results demonstrate that our scheme can achieve better rate-distortion performance than some of state-of-the-art schemes.     

A Proxy Re-Encryption with Keyword Search Scheme in Cloud Computing

With the widespread use of cloud computing technology, more and more users and enterprises decide to store their data in a cloud server by outsourcing. However, these huge amounts of data may contain personal privacy, business secrets and other sensitive information of the users and enterprises. Thus, at present, how to protect, retrieve, and legally use the sensitive information while preventing illegal accesses are security challenges of data storage in the cloud environment. A new proxy re-encryption with keyword search scheme is proposed in this paper in order to solve the problem of the low retrieval efficiency of the encrypted data in the cloud server. In this scheme, the user data are divided into files, file indexes and the keyword corresponding to the files, which are respectively encrypted to store. The improved scheme does not need to re-encrypt partial file cipher-text as in traditional schemes, but re-encrypt the cipher-text of keywords corresponding to the files. Therefore the scheme can improve the computational efficiency as well as resist chosen keyword attack. And the scheme is proven to be indistinguishable under Hash Diffie-Hellman assumption. Furthermore, the scheme does not need to use any secure channels, making it more effective in the cloud environment.     

An Encrypted Image Retrieval Method Based on SimHash in Cloud Computing

With the massive growth of images data and the rise of cloud computing that can provide cheap storage space and convenient access, more and more users store data in cloud server. However, how to quickly query the expected data with privacy-preserving is still a challenging in the encryption image data retrieval. Towards this goal, this paper proposes a ciphertext image retrieval method based on SimHash in cloud computing. Firstly, we extract local feature of images, and then cluster the features by K-means. Based on it, the visual word codebook is introduced to represent feature information of images, which hashes the codebook to the corresponding fingerprint. Finally, the image feature vector is generated by SimHash searchable encryption feature algorithm for similarity retrieval. Extensive experiments on two public datasets validate the effectiveness of our method. Besides, the proposed method outperforms one popular searchable encryption, and the results are competitive to the state-of-the-art.     

Reversible Data Hiding in Encrypted Images Based on Prediction and Adaptive Classification Scrambling

Reversible data hiding in encrypted images (RDH-EI) technology is widely used in cloud storage for image privacy protection. In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images, we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling. First, the prediction error image is obtained by a novel prediction method before encryption. Then, the image pixel values are divided into two categories by the threshold range, which is selected adaptively according to the image content. Multiple high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain unchanged. The optimal threshold selected adaptively ensures the maximum embedding capacity of the algorithm. Moreover, the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel position. Experiment results demonstrate that the proposed method has higher embedding capacity compared with the current state-ofthe-art methods for images with different texture complexity.     

A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed. In cloud computation, data processing, storage, and transmission can be done through laptops and mobile devices. Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients. The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s data. They have to be dependent on cloud service providers for assurance of the platform’s security. Data security and privacy issues reduce the progression of cloud computing and add complexity. Nowadays; most of the data that is stored on cloud servers is in the form of images and photographs, which is a very confidential form of data that requires secured transmission. In this research work, a public key cryptosystem is being implemented to store, retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman (RSA) algorithm for the encryption and decryption of data. The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment. To enhance the user data security level, a neural network is used for user authentication and recognition. Moreover; the proposed technique develops the performance of detection as a loss function of the bounding box. The Faster Region-Based Convolutional Neural Network (Faster R-CNN) gets trained on images to identify authorized users with an accuracy of 99.9% on training.     

High Capacity Data Hiding in Encrypted Image Based on Compressive Sensing for Nonequivalent Resources

To fulfill the requirements of data security in environments with nonequivalent resources, a high capacity data hiding scheme in encrypted image based on compressive sensing (CS) is proposed by fully utilizing the adaptability of CS to nonequivalent resources. The original image is divided into two parts: one part is encrypted with traditional stream cipher; the other part is turned to the prediction error and then encrypted based on CS to vacate room simultaneously. The collected non-image data is firstly encrypted with simple stream cipher. For data security management, the encrypted non-image data is then embedded into the encrypted image, and the scrambling operation is used to further improve security. Finally, the original image and non-image data can be separably recovered and extracted according to the request from the valid users with different access rights. Experimental results demonstrate that the proposed scheme outperforms other data hiding methods based on CS, and is more suitable for nonequivalent resources.     

Research on Electronic Document Management System Based on Cloud Computing

With the development of information technology, cloud computing technology has brought many conveniences to all aspects of work and life. With the continuous promotion, popularization and vigorous development of e-government and e-commerce, the number of documents in electronic form is getting larger and larger. Electronic document is an indispensable main tool and real record of e-government and business activities. How to scientifically and effectively manage electronic documents? This is an important issue faced by governments and enterprises in improving management efficiency, protecting state secrets or business secrets, and reducing management costs. This paper discusses the application of cloud computing technology in the construction of electronic file management system, proposes an architecture of electronic file management system based on cloud computing, and makes a more detailed discussion on key technologies and implementation. The electronic file management system is built on the cloud architecture to enable users to upload, download, share, set security roles, audit, and retrieve files based on multiple modes. An electronic file management system based on cloud computing can make full use of cloud storage, cloud security, and cloud computing technologies to achieve unified, reliable, and secure management of electronic files.     

Reversible Data Hiding in Classification-Scrambling Encrypted-Image Based on Iterative Recovery

To improve the security and quality of decrypted images, this work proposes a reversible data hiding in encrypted image based on iterative recovery. The encrypted image is firstly generated by the pixel classification scrambling and bit-wise exclusive-OR (XOR), which improves the security of encrypted images. And then, a pixel-type-mark generation method based on block-compression is designed to reduce the extra burden of key management and transfer. At last, an iterative recovery strategy is proposed to optimize the marked decrypted image, which allows the original image to be obtained only using the encryption key. The proposed reversible data hiding scheme in encrypted image is not vulnerable to the ciphertext-only attack due to the fact that the XOR-encrypted pixels are scrambled in the corresponding encrypted image. Experimental results demonstrate that the decrypted images obtained by the proposed method are the same as the original ones, and the maximum embedding rate of proposed method is higher than the previously reported reversible data hiding methods in encrypted image.     

An Effective Steganalysis Algorithm for Histogram-Shifting Based Reversible Data Hiding

To measure the security for hot searched reversible data hiding (RDH) technique, especially for the common-used histogram-shifting based RDH (denoted as HS-RDH), several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking image. However, conventional steganalysis schemes focused on the previous RDH algorithms, i.e., some early spatial/pixel domain-based histogram-shifting (HS) schemes, which might cause great changes in statistical characteristics and thus be easy to be detected. For recent improved methods, such as some adaptive prediction error (PE) based embedding schemes, those conventional schemes might be invalid, since those adaptive embedding mechanism would effectively reduce the embedding trace and thus increase the difficulty of steganalysis. Therefore, a novel steganalysis method is proposed in this paper to detect recent adaptive RDH schemes and provide a more effective detection tool for RDH. The contributions of this paper could be summarized as follows. (1) By analyzing the characteristics for those adaptive HS-RDH, an effective “flat ground” based detection method is designed to fast identify whether the given image is used to hide secret data; (2) According to the empirical statistical model, double check mechanism is provided to improve the detection accuracy; (3) In addition, to further improve detection ability, some detailed information for secret data, i.e., its content and embedding location are further estimated. Compared with conventional steganalysis methods, experimental results indicate that our proposed algorithm could achieve a better detection accuracy and meanwhile acquire more detailed information on secret data.     

Reversible Data Hiding Based on Pixel-Value-Ordering and Pixel Block Merging Strategy

With the reversible data hiding method based on pixel-value-ordering, data are embedded through the modification of the maximum and minimum values of a block. A significant relationship exists between the embedding performance and the block size. Traditional pixel-value-ordering methods utilize pixel blocks with a fixed size to embed data; the smaller the pixel blocks, greater is the embedding capacity. However, it tends to result in the deterioration of the quality of the marked image. Herein, a novel reversible data hiding method is proposed by incorporating a block merging strategy into Li et al.’s pixel-value-ordering method, which realizes the dynamic control of block size by considering the image texture. First, the cover image is divided into non-overlapping 2×2 pixel blocks. Subsequently, according to their complexity, similarity and thresholds, these blocks are employed for data embedding through the pixel-value-ordering method directly or after being emerged into 2×4, 4×2, or 4×4 sized blocks. Hence, smaller blocks can be used in the smooth region to create a high embedding capacity and larger blocks in the texture region to maintain a high peak signal-to-noise ratio. Experimental results prove that the proposed method is superior to the other three advanced methods. It achieves a high embedding capacity while maintaining low distortion and improves the embedding performance of the pixel-value-ordering algorithm.     

基于云计算和大数据教学实训平台的设计研究

云计算、大数据是当前信息产业和教育领域的热点。云计算的核心技术是基于虚拟化的技术,而大数据的核心基础是基于集群的计算技术。论文围绕云计算与大数据的核心特点,设计实现一套适合教学实训的云计算大数据硬件实验平台。平台采用瘦终端＋集群的硬件解决方案,构建虚拟化平台,以满足普通的教学实验,并构建虚拟化实验环境;在此基础上构建多种集群计算环境,以满足大数据和高性能计算教学实训的要求。本平台融合虚拟化和集群计算的特点,提供统一的解决方案,具有一体化、高密度、多平台、高性价比等特点。     

An Advanced Analysis of Cloud Computing Concepts Based on the Computer Science Ontology

Our primary research hypothesis stands on a simple idea: The evolution of top-rated publications on a particular theme depends heavily on the progress and maturity of related topics. And this even when there are no clear relations or some concepts appear to cease to exist and leave place for newer ones starting many years ago. We implemented our model based on Computer Science Ontology (CSO) and analyzed 44 years of publications. Then we derived the most important concepts related to Cloud Computing (CC) from the scientific collection offered by Clarivate Analytics. Our methodology includes data extraction using advanced web crawling techniques, data preparation, statistical data analysis, and graphical representations. We obtained related concepts after aggregating the scores using the Jaccard coefficient and CSO Ontology. Our article reveals the contribution of Cloud Computing topics in research papers in leading scientific journals and the relationships between the field of Cloud Computing and the interdependent subdivisions identified in the broader framework of Computer Science.     

A General Framework of Reversible Data Hiding with Controlled Contrast Enhancement

This paper proposes a two-step general framework for reversible data hiding (RDH) schemes with controllable contrast enhancement. The first step aims at preserving visual perception as much as possible on the basis of achieving high embedding capacity (EC), while the second step is used for increasing image contrast. In the second step, some peak-pairs are utilized so that the histogram of pixel values is modified to perform histogram equalization (HE), which would lead to the image contrast enhancement. However, for HE, the utilization of some peak-pairs easily leads to over-enhanced image contrast when a large number of bits are embedded. Therefore, in our proposed framework, contrast over-enhancement is avoided by controlling the degree of contrast enhancement. Since the second step can only provide a small amount of data due to controlled contrast enhancement, the first one helps to achieve a large amount of data without degrading visual quality. Any RDH method which can achieve high EC while preserve good visual quality, can be selected for the first step. In fact, Gao et al.’s method is a special case of our proposed framework. In addition, two simple and commonly-used RDH methods are also introduced to further demonstrate the generalization of our framework.     

以标准化手段助推广州云计算落地发展

本文综述了广州市云计算发展现状及存在问题,并在此基础上,结合标准化实践经验,提出了加快广州云计算落地发展的相关建议,以期在全球云计算信息变革中,能为广州抢占云计算产业发展制高点,提高国家中心城市竞争力,建设智慧广州提供参考。     

XML-Based Information Fusion Architecture Based on Cloud Computing Ecosystem

Considering cloud computing from an organizational and end user computing point of view, it is a new paradigm for deploying, managing and offering services through a shared infrastructure. Current development of cloud computing applications, however, are the lack of a uniformly approach to cope with the heterogeneous information fusion. This leads cloud computing to inefficient development and a low potential reuse. This study addresses these issues to propose a novel Web 2.0 Mashups as a Service, called WMaaS, which is a fundamental cloud service model. The WMaaS is developed based on a XML-based Mashups Architecture (XMA) that is composed of Web 2.0 Mashups technologies, including Web Data, Web API, Web Interaction, and Web Presentation to associate with existing service models. To demonstrate the feasibility of this approach, this study implemented a Ubiquitous Location-based Service System (ULSS) that is a cloud computing application developed based on WMaaS to provide continuous and location-based schedule information for organization monitoring and end user needs.     

Cloud Data Center Selection Using a Modified Differential Evolution

The interest in selecting an appropriate cloud data center is exponentially increasing due to the popularity and continuous growth of the cloud computing sector. Cloud data center selection challenges are compounded by ever-increasing users’ requests and the number of data centers required to execute these requests. Cloud service broker policy defines cloud data center’s selection, which is a case of an NP-hard problem that needs a precise solution for an efficient and superior solution. Differential evolution algorithm is a metaheuristic algorithm characterized by its speed and robustness, and it is well suited for selecting an appropriate cloud data center. This paper presents a modified differential evolution algorithm-based cloud service broker policy for the most appropriate data center selection in the cloud computing environment. The differential evolution algorithm is modified using the proposed new mutation technique ensuring enhanced performance and providing an appropriate selection of data centers. The proposed policy’s superiority in selecting the most suitable data center is evaluated using the CloudAnalyst simulator. The results are compared with the state-of-arts cloud service broker policies.     

Heuristic and Bent Key Exchange Secured Energy Efficient Data Transaction for Traffic Offloading in Mobile Cloud

In today’s world, smart phones offer various applications namely face detection, augmented-reality, image and video processing, video gaming and speech recognition. With the increasing demand for computing resources, these applications become more complicated. Cloud Computing (CC) environment provides access to unlimited resource pool with several features, including on demand self-service, elasticity, wide network access, resource pooling, low cost, and ease of use. Mobile Cloud Computing (MCC) aimed at overcoming drawbacks of smart phone devices. The task remains in combining CC technology to the mobile devices with improved battery life and therefore resulting in significant performance. For remote execution, recent studies suggested downloading all or part of mobile application from mobile device. On the other hand, in offloading process, mobile device energy consumption, Central Processing Unit (CPU) utilization, execution time, remaining battery life and amount of data transmission in network were related to one or more constraints by frameworks designed. To address the issues, a Heuristic and Bent Key Exchange (H-BKE) method can be considered by both ways to optimize energy consumption as well as to improve security during offloading. First, an energy efficient offloading model is designed using Reactive Heuristic Offloading algorithm where, the secondary users are allocated with the unused primary users’ spectrum. Next, a novel AES algorithm is designed that uses a Bent function and Rijndael variant with the advantage of large block size is hard to interpret and hence is said to ensure security while accessing primary users’ unused spectrum by the secondary user. Simulations are conducted for efficient offloading in mobile cloud and performance valuations are carried on the way to demonstrate that our projected technique is successful in terms of time consumption, energy consumption along with the security aspects covered during offloading in MCC.     

Reversible Data Hiding Based on Run-Level Coding in H.264/AVC Video Streams

This paper presents a reversible data hiding (RDH) method, which is designed by combining histogram modification (HM) with run-level coding in H.264/advanced video coding (AVC). In this scheme, the run-level is changed for embedding data into H.264/AVC video sequences. In order to guarantee the reversibility of the proposed scheme, the last nonzero quantized discrete cosine transform (DCT) coefficients in embeddable 4×4 blocks are shifted by the technology of histogram modification. The proposed scheme is realized after quantization and before entropy coding of H.264/AVC compression standard. Therefore, the embedded information can be correctly extracted at the decoding side. Peak-signal-noise-to-ratio (PSNR) and Structure similarity index (SSIM), embedding payload and bit-rate variation are exploited to measure the performance of the proposed scheme. Experimental results have shown that the proposed scheme leads to less SSIM variation and bit-rate increase.     

Cloud Data Encryption and Authentication Based on Enhanced Merkle Hash Tree Method

Many organizations apply cloud computing to store and effectively process data for various applications. The user uploads the data in the cloud has less security due to the unreliable verification process of data integrity. In this research, an enhanced Merkle hash tree method of effective authentication model is proposed in the multi-owner cloud to increase the security of the cloud data. Merkle Hash tree applies the leaf nodes with a hash tag and the non-leaf node contains the table of hash information of child to encrypt the large data. Merkle Hash tree provides the efficient mapping of data and easily identifies the changes made in the data due to proper structure. The developed model supports privacy-preserving public auditing to provide a secure cloud storage system. The data owners upload the data in the cloud and edit the data using the private key. An enhanced Merkle hash tree method stores the data in the cloud server and splits it into batches. The data files requested by the data owner are audit by a third-party auditor and the multi-owner authentication method is applied during the modification process to authenticate the user. The result shows that the proposed method reduces the encryption and decryption time for cloud data storage by 2–167 ms when compared to the existing Advanced Encryption Standard and Blowfish.     

基于云计算关键技术的标准信息资源数据中心服务模式研究

本文采用了分而治之的思想,依靠众多的标准信息提供商,利用云计算技术,建设廉价、可靠,具备可管理性、可伸缩性的标准信息资源数据中心,为企业或公众提供按数据流量付费的服务模式.标准资源分散存储在各分站点上,但统一由总部管理和提供服务,所收取的服务费按照各分站点的资源访问和贡献情况进行分配,各分站点再根据版权协议把费用付给标准信息提供机构或个人.最后给出应用于专业镇标准信息服务的两个案例.