Nature - Inspired enhanced data deduplication for efficient cloud storage

Cloud computing has empowered the individual user by providing seemingly unlimited storage space and availability and accessibility of data anytime and anywhere. Cloud service providers are able to maximize data storage space by incorporating data deduplication into cloud storage. Although data deduplication removes data redundancy and data replication, it also introduces major data privacy and security issues for the user. In this paper, a new privacy-preserving framework that addresses this issue is proposed. Our framework uses an efficient deduplication algorithm to divide a given file into smaller units. These units are then encrypted by the user using the combination of a secure hash function and a block encryption algorithm. An index tree of hash values of these units is also generated and encrypted using an asymmetric search encryption scheme by the user. This index tree will enable the cloud service provider to search through the index and return the requested units. We will show that our proposed framework will allow cloud service and storage providers to employ data deduplication techniques without giving them access to either the users' plaintexts or the users' decryption keys.

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach

In today's digital era, the exponential growth of data necessitates effective storage and management solutions. The cloud has vast storage possibilities to store huge amounts of data. Public access to the cloud leads to duplicate copies of data stored in the storage. Maintaining a single copy of data in the cloud is most important for efficient data storage management. This paper introduces a groundbreaking strategy for improving the efficacy of cloud storage through innovative data deduplication techniques at the block levels. The block-level duplication verification efficiently identifies the duplicate data in the storage. It helps to protect the duplicate storage in the cloud data storage infrastructure. The block-level deduplication technique uses variable-length blocks based on the duplicate content of the block. Initially, A file is divided into a number of blocks with a size of 5kb. According to the proposed method, If any block is partially matched with a block already stored in the cloud, then that block is further divided into smaller blocks based on the matching percentage. The smaller blocks help to deduplicate the data more effectively. The work is implemented in a live cloud setting with a C# application hosted on MyASP.NET. The proposed methodology's effectiveness is validated against existing deduplication techniques. The results reveal a marked improvement in storage utilization and data management, affirming the potential of the approach to revolutionize cloud storage efficiency.

Payment-based incentive mechanism for secure cloud deduplication

<p>Symbolic regression (SR) is a function identification process, the task of which is to identify and express the relationship between the input and output variables in mathematical models. SR is named to emphasise its ability to find the structure and coefficients of the model simultaneously. Genetic Programming (GP) is an attractive and powerful technique for SR, since it does not require any predefined model and has a flexible representation. However, GP based SR generally has a poor generalisation ability which degrades its reliability and hampers its applications to science and real-world modeling. Therefore, this thesis aims to develop new GP approaches to SR that evolve/learn models exhibiting good generalisation ability. This thesis develops a novel feature selection method in GP for high-dimensional SR. Feature selection can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalisation ability. However, feature selection is seldom considered in GP for high-dimensional SR. The proposed new feature selection method utilises GP’s built-in feature selection ability and relies on permutation to detect the truly relevant features and discard irrelevant/noisy features. The results confirm the superiority of the proposed method over the other examined feature selection methods including random forests and decision trees on identifying the truly relevant features. Further analysis indicates that the models evolved by GP with the proposed feature selection method are more likely to contain only the truly relevant features and have better interpretability. To address the overfitting issue of GP when learning from a relatively small number of instances, this thesis proposes a new GP approach by incorporating structural risk minimisation (SRM), which is a framework to estimate the generalisation performance of models, into GP. The effectiveness of SRM highly depends on the accuracy of the Vapnik-Chervonenkis (VC) dimension measuring model complexity. This thesis significantly extends an experimental method (instead of theoretical estimation) to measure the VC-dimension of a mixture of linear and nonlinear regression models in GP for the first time. The experimental method has been conducted using uniform and non-uniform settings and provides reliable VC-dimension values. The results show that our methods have an impressively better generalisation gain and evolve more compact model, which have a much smaller behavioural difference from the target models than standard GP and GP with bootstrap, The proposed method using the optimised non-uniform setting further improves the one using the uniform setting. This thesis employs geometric semantic GP (GSGP) to tackle the unsatisfied generalisation performance of GP for SR when no overfitting occurs. It proposes three new angle-awareness driven geometric semantic operators (GSO) including selection, crossover and mutation to further explore the geometry of the semantic space to gain a greater generalisation improvement in GP for SR. The angle-awareness brings new geometric properties to these geometric operators, which are expected to provide a greater leverage for approximating the target semantics in each operation, and more importantly, to be resistant to overfitting. The results show that compared with two kinds of state-of-the-art GSOs, the proposed new GSOs not only drive the evolutionary process fitting the target semantics more efficiently but also significantly improve the generalisation performance. A further comparison on the evolved models shows that the new method generally produces simpler models with a much smaller size and containing important building blocks of the target models.</p>

With the continuous development of Internet technology, cloud storage has been widely applied and developed. Users can store data through the network anytime, anywhere. However, the frequent occurrence of a series of cloud storage data security events show that cloud storage not only provides data storage services for users, but also poses huge threats and challenges to users' data security. Aiming at the problem of user data security in cloud storage environment, the system introduces multi-cloud storage system on the basis of the traditional single cloud storage system. The system uses Erasure Code to block the original data, and encrypts the data blocks using AES (Advanced Encryption Standard) encryption technology, then stores the encrypted data blocks in different cloud storages terminals. Thus, the hidden danger of data loss or leakage due to relying on a cloud service provider is eliminated, the existing security problems of cloud storage system are improved, and the security and reliability of the multi-cloud secure storage system are improved.

An advancement in computing qualities that are both economical and powerful have been made possible by the Internet's dramatic development in processing and storage methods. A new technology that offers internet application access and data storage, cloud computing is becoming more and more popular. Both opportunities and challenges abound in this system. Two extremely important issues that need to be addressed are data security and the growing amount of similar data duplication in cloud. A cloud computing system, supported via distributed & virtual machine technology, offers the most comprehensive services in the most effective way. When using the cloud technology, customers may access their limitless storage space from any location at any time. Cloud service providers are responsive to customer requests to increase data storage capacity by integrating cloud systems with deduplication techniques, while deduplication techniques erase redundant data that is available in cloud environments, according to the company. Preserving data privacy is another significant issue that needs to be acknowledged for its significance. For reducing duplicate data quantity in storage system, the deduplication technique was developed. This study offers a novel method for eliminating duplicate or repetitive information from cloud servers, that might aid in lowering storage space amount and needed bandwidth. It was illustrated that by using testing outcomes, proposed model eliminates the main drawbacks of the existing methods while also offering increased security for data kept in cloud storage

Activity recognition from smartphone sensor inputs is of great importance to enhance user experience. Our study aims to investigate the applicability of Genetic Programming (GP) approach on this complex real world problem. Traditional methods often require substantial human efforts to define good features. Moreover the optimal features for one type of activity may not be suitable for another. In comparison, our GP approach does not require such feature extraction process, hence, more suitable for complex activities where good features are difficult to be pre-defined. To facilitate this study we therefore propose a benchmark of activity data collected from various smartphone sensors, as currently there is no existing publicly available database for activity recognition. In this study, a GP-based approach is applied to nine types of activity recognition tasks by directly taking raw data instead of features. The effectiveness of this approach can be seen by the promising results. In addition our benchmark data provides a platform for other machine learning algorithms to evaluate their performance on activity recognition.

JCDC: A blockchain-based framework for secure data storage and circulation in JointCloud

In spite of the growing usage of smart mobile devices, the inherent restrictions like resource scarcity, mobility and frequent disconnections makes it difficult to exploit its full potential. Cloud computing simplifies the deployment of large-scale distributed systems and enables advanced computational power with improved storage capabilities. The evolution of Mobile Cloud Computing (MCC) exploits the services of Cloud Service Providers (CSP) by outsourcing the local data storage and computation to the remote servers and thereby relieving the overhead incurred by mobile clients for data storage and processing. In spite of the relief, the clients no longer have control over their outsourced data which demands the purpose of ensuring the data intactness maintained by the Cloud Storage Server (CSS). The resource constrained mobile devices act as a thin client for the cloud server access and hence the computation for ensuring the proof of correctness for secure data storage becomes an overhead with existing solutions. The proposed auditing protocol introduces a trusted third party auditor (TPA) who will be in charge of verifying the correctness of data stored at cloud storage on behalf of the user without the need for retrieving the entire data. The usage of Bilinear pairing and Merkle Hash Trees guarantees Blockless verification by TPA. The verification process in the proposed method reduces the auditing overhead at the mobile device using short signatures. The proposed auditing protocol saves the mobile client's computational resource, thereby achieving cost-effectiveness to gain trust in the Cloud Storage Service.

This paper presents a novel nonparametric efficiency analysis technique based on the Genetic Programming (GP) in order to measure efficiency of Iran electric power plants. GP model was used to predict the output of power plants with respect to input data. The method, we presented here, is capable of finding a best performance among power plant based on the set of input data, GP predicted results and real outputs. The advantage of using GP over traditional statistical methods is that in prediction with GP, the researcher doesn’t need to assume the data characteristic of the dependent variable or output and the independent variable or input. In this proposed methodology to calculate the efficiency scores, a novel algorithm was introduced which worked on the basis of predicted and real output values. To validate our model, the results of proposed algorithm for calculating efficiency rank of power plants were compared to traditional method. Real data was presented for illustrative our proposed methodology. Results showed that by utilizing the capability of input-output pattern recognition of GP, this method provides more realistic results and outperform in identification of efficient units than the conventional methods.

Organizations worldwide commonly utilize cloud infrastructure to manage large volumes of data, making the optimization of storage crucial for enhancing cloud performance. One effective optimization technique is data deduplication, which identifies duplicate objects and ensures that only one copy of unique data is stored in the cloud. While several deduplication schemes currently exist, there is a pressing need to improve efficiency in cloud storage through innovative approaches. In this paper, we propose a new system model designed to facilitate an efficient deduplication process. Our algorithm, called deduplication in cloud infrastructure (DCI), offers a systematic and effective method for handling deduplication challenges related to redundant data storage. DCI focuses on hash generation, metadata comparison, and pointer-based deduplication, providing a comprehensive strategy for optimizing cloud storage resources and minimizing duplication. This ultimately enhances both the efficiency and cost-effectiveness of cloud-based data management. A simulation study using CloudSim and the Hadoop distributed file system (HDFS) simulator demonstrates that the proposed deduplication method is effective. Experimental results show that our algorithm outperforms many existing solutions, achieving the highest deduplication ratio of 6.7 and saving 85.09% of storage space due to its efficient deduplication approach. The proposed system can be used in cloud infrastructures for efficiency.

Using cloud data clients may transfer data from their computer systems to cloud servers. Hence, user will not have any burden of maintenance and also he gets high quality data storage services. Many security issues are concerned with cloud storage. Cloud service providers or storage servers are not completely trustworthy. The user is concerned about the information stored on cloud are in place or not turns. This paper is based on homomorphic hash algorithm. Further this supports dynamic operations such as insert, update, delete and modify at block level, for data dynamics Merkle Hash Tree is used which helps to find the location of each dynamic operation. Third party auditor checks the user's data for correctness and gives the accuracy of the data that is stored in cloud server. The communication and computation overhead are reduced. Deduplication technique is used to check whether the file that user need to store in cloud storage is already exist at cloud server or not. This framework is effective and secure against replace attack launch by malicious server.

We present a control plane architecture to enable software-addressable optical acceleration from the application layer. The architecture is experimentally examined on a cluster-computing test-bed by enabling physical layer optical multicasting on-demand for the application layer to achieve non-blocking performance.