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

MSIGT: Most Significant Index Generation Technique for cloud environment

Cloud Computing is a computing paradigm for delivering computational power, storage and applications as services via Internet on a pay-as-you-go basis to consumers. The data owner outsources local data to the public cloud server to reduce the cost of the data management. Critical data has to be encrypted to ensure privacy before outsourcing. The state-of-the-art SSE schemes search only over encrypted data through keywords, hence they do not provide effective data utilisation for large dataset files in cloud. We propose a Most Significant Index Generation Technique (MSIGT), that supports secure and efficient index generation time using a Most Significant Digit (MSD) radix sort. MSD radix sort is simple and faster in sorting array strings. A mathematical model is developed to encrypt the indexed keywords for secure index generation without the overhead of learning from the attacker/cloud provider. It is seen that the MSIGT scheme can reduce the cost of data on owner side to O(NT × 3) with a score calculation of O(NT). The proposed scheme is effective and efficient in comparison with the existing algorithms

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

Secure Cloud Computing for Solving Large-Scale Linear Systems of Equations

Solving large-scale linear systems of equations (LSEs) is one of the most common and fundamental problems in big data. But such problems are often too expensive to solve for resource-limited users. Cloud computing has been proposed as an efficient and costeffective way of solving such tasks. Nevertheless, one critical concern in cloud computing is data privacy. Many previous works on secure outsourcing of LSEs have high computational complexity and share a common serious problem, i.e., a huge number of external memory I/O operations, which may render those outsourcing schemes impractical. We develop a practical secure outsourcing algorithm for solving large-scale LSEs, which has both low computational complexity and low memory I/O complexity and can protect clients privacy well. We implement our algorithm on a real-world cloud server and a laptop. We find that the proposed algorithm offers significant time savings for the client (up to 65%) compared to previous algorithms

AnonyControl: Control Cloud Data Anonymously with Multi-Authority Attribute-Based Encryption

Cloud computing is a revolutionary computing paradigm which enables flexible, on-demand and low-cost usage of computing resources. However, those advantages, ironically, are the causes of security and privacy problems, which emerge because the data owned by different users are stored in some cloud servers instead of under their own control. To deal with security problems, various schemes based on the Attribute- Based Encryption (ABE) have been proposed recently. However, the privacy problem of cloud computing is yet to be solved. This paper presents an anonymous privilege control scheme AnonyControl to address the user and data privacy problem in a cloud. By using multiple authorities in cloud computing system, our proposed scheme achieves anonymous cloud data access, finegrained privilege control, and more importantly, tolerance to up to (N -2) authority compromise. Our security and performance analysis show that AnonyControl is both secure and efficient for cloud computing environment.Comment: 9 pages, 6 figures, 3 tables, conference, IEEE INFOCOM 201

Achieving Privacy Assured Outsourcing of Data in Cloud Using Optimalvisual Cryptography

Abstract-Security has emerged as the most feared aspect of cloud computing and a major hindrance for the customers. In existing system for establishing secure and privacy-assured service outsourcing in cloud computing which uses Linear programming and compressed sensing techniques to transform images, which aims to take security, complexity, and efficiency into consideration from the very beginning of the service flow. But it makes more complexity because the data is sent in its raw form to one cloud. The cryptography schemes are computationally more complex. In order to enhance the security and reduce the complexity, to use data obfuscation through a novel visual cryptography. A conventional threshold (k out of n) visual secret sharing scheme encodes one secret image into transparencies (called shares) such that any group of transparencies reveals when they are superimposed, while that of less than ones cannot. In the proposed work, novel multiple secret visual cryptographic schemes are used to encode the secret s images into n shares. Convert the data into basic images and send the encrypted form of image by using multiple visual cryptographic schemes. (k, n, s) -MVCS, in which the superimposition of each group of shares reveals the first, second, s th secret, respectively where s=n-k+1. The proposed system also considers visual cryptography without pixel expansion. A new scheme for visual cryptography is developed and configured for the cloud for storing and retrieving textual data. Testing the system with query execution on a cloud database indicates full accuracy in record retrievals with negligible false positives. In addition, the system is resilient to attacks from within and outside the cloud. An experimental result shows that the Complexity analysis, Security analysis, the system is tested against artificial intelligence/machine learning based attacks

AN EFFICIENT SEARCH SCHEME OVER ENCRYPTED DATA ON MOBILE CLOUD

Cloud gives a climbable, convenient and huge measure of capacity. The alluring innovation be done requiring little to no effort. Information protection is the real downsides that keeps client from putting away record in the cloud with trustful way. The method for improving protection from information proprietor perspective is to scramble and unscramble the record subsequent to downloading them and information encryption makes substantial overhead the cell phones and anyway information recovery acquires muddled correspondence between information client and the cloud. The fundamental issue is that in versatile it gives restricted battery life and constrained transfer speed limit and result in correspondence and registering issue. Subsequently it prompts scrambled hunt over versatile cloud an extremely difficult one. Keeping in mind the end goal to keep this issues Traffic and Energy sparing Encrypted Search (TEES )a data transfer capacity and vitality sparing scrambled inquiry over portable cloud is proposed. An encoded seek design offloads the calculation from cell phone to cloud. It likewise enhances the correspondence between versatile customer and cloud. Information protection does not debase when execution improvement is connected. An encoded seek decreases the calculation time 23% to 46% and spare the vitality utilization 35% to 55% for every record recovery and system movement amid document recovery are to be altogether diminished