Secured Data Outsourcing in Cloud Computing

Cloud computing is a popular technology in the IT world. After internet, it is the biggest thing for IT world. Cloud computing uses the Internet for performing the task on the computer and it is the next- generation architecture of IT Industry. It is related to different technologies and the convergence of various technologies has emerged to be called as cloud computing. It places the application software and databases to the huge data centers, where the supervision of the data and services may not be fully trusted. This unique attribute poses many new security challenges which have not been well understood. In this paper, we develop system which allows customer to use cloud server with various profits and strong securities. So when customer stores his sensitive data on cloud server he should not worry about securities, we also protect customer’s account from malicious behaviors by verifying the result. This result verification mechanism is highly efficient for both cloud server and cloud customer. Covering security analysis and experiment results shows the immediate practicability of our mechanism design. DOI: 10.17762/ijritcc2321-8169.150314

The Protected Optimization Totaling Outsourcing In A Case Study Of Linear Programming

This researches secure outsourcing of generally relevant linear programming (LP) calculations. Our instrument configuration expressly disintegrates LP calculation outsourcing into open LP solvers running on the cloud and private LP parameters possessed by the client. The subsequent adaptability enables us to investigate suitable security/productivity tradeoff by means of more elevated amount reflection of LP calculation than the general circuit portrayal. In particular, by planning private LP issue as an arrangement of grids/vectors, we create productive security saving issue change procedures, which enable clients to change the first LP into some arbitrary one while ensuring sensitive input/output data

Two Efficient outsourced ABS (OABS) Schemes Denoted By OABS-I And OABS-II To Build An Efficient outsourced Verifying Protocol

Attribute-based signature (ABS) facilitates revelry to mark a message with fine-grained access control over make out information. Particularly, in an ABS system, users get hold of their attribute private keys from an attribute authority, with which they can presently sign messages for any predicate fulfilled by their attributes. We first advise and formalize a new concept called Outsourced ABS, i.e., OABS, in which the computational overhead at user side is really concentrated through outsourcing concentrated computations to an untrusted signing-cloud service provider (S-CSP). In addition, we be relevant this novel paradigm to existing ABS schemes to decrease the difficulty

Secure Optimization Computation Outsourcing in Cloud Computing: A Case Study of Linear Programming

Abstract-Cloud computing enables an economically promising paradigm of computation outsourcing. However, how to protect customers confidential data processed and generated during the computation is becoming the major security concern. Focusing on engineering computing and optimization tasks, this paper investigates secure outsourcing of widely applicable linear programming (LP) computations. Our mechanism design explicitly decomposes LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer. The resulting flexibility allows us to explore appropriate security/efficiency tradeoff via higher-level abstraction of LP computation than the general circuit representation. Specifically, by formulating private LP problem as a set of matrices/vectors, we develop efficient privacy-preserving problem transformation techniques, which allow customers to transform the original LP into some random one while protecting sensitive input/output information. To validate the computation result, we further explore the fundamental duality theorem of LP and derive the necessary and sufficient conditions that correct results must satisfy. Such result verification mechanism is very efficient and incurs close-to-zero additional cost on both cloud server and customers. Extensive security analysis and experiment results show the immediate practicability of our mechanism design

Preserving Privacy for Secure and Outsourcing for Linear Programming in Cloud Computing

Abstract Cloud computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. we utilize the public key based homomorphism authenticator and uniquely integrate it with random mask technique to achieve a privacy-preserving public auditing system for cloud data storage security while keeping all above requirements in mind. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously along with investigates secure outsourcing of widely applicable linear programming (LP) computations. In order to achieve practical efficiency, our mechanism design explicitly decomposes the LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient

Secure Outsourced Computation of the Characteristic Polynomial and Eigenvalues of Matrix

Linear algebra plays an important role in computer science, especially in cryptography.Numerous cryptog-raphic protocols, scientific computations, and numerical computations are based on linear algebra. Many linear algebra tasks can be reduced to some core problems, such as matrix multiplication, determinant of matrix and the characteristic polynomial of matrix. However, it is difficult to execute these tasks independently for client whose computation abilities are weaker than polynomial-time computational ability. Cloud Computing is a novel economical paradigm which provides powerful computational resources that enables resources-constrained client to outsource their mass computing tasks to the cloud. In this paper, we propose a new verifiable and secure outsourcing protocol for the problem of computing the characteristic polynomial and eigenvalues of matrix. These protocols are not only efficient and secure, but also unnecessary for any cryptographic assumption

Practical and Secure Outsourcing Algorithms of Matrix Operations Based on a Novel Matrix Encryption Method

With the recent growth and commercialization of cloud computing, outsourcing computation has become one of the most important cloud services, which allows the resource-constrained clients to efficiently perform large-scale computation in a pay-per-use manner. Meanwhile, outsourcing large scale computing problems and computationally intensive applications to the cloud has become prevalent in the science and engineering computing community. As important fundamental operations, large-scale matrix multiplication computation (MMC), matrix inversion computation (MIC), and matrix determinant computation (MDC) have been frequently used. In this paper, we present three new algorithms to enable secure, verifiable, and efficient outsourcing of MMC, MIC, and MDC operations to a cloud that may be potentially malicious. The main idea behind our algorithms is a novel matrix encryption/decryption method utilizing consecutive and sparse unimodular matrix transformations. Compared to previous works, this versatile technique can be applied to many matrix operations while achieving a good balance between security and efficiency. First, the proposed algorithms provide robust confidentiality by concealing the local information of the entries in the input matrices. Besides, they also protect the statistic information of the original matrix. Moreover, these algorithms are highly efficient. Our theoretical analysis indicates that the proposed algorithms reduce the time overhead on the client side from O(n 2.3728639 ) to O(n 2 ). Finally, the extensive experimental evaluations demonstrate the practical efficiency and effectiveness of our algorithms