Secure Outsourcing of Linear Programming Solver in Cloud Computing: A Survey (original) (raw)
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Secure Outsourcing of Linear Programming in Cloud Computing Environment: A Review
Cloud computing provides immense computing power with reduced cost. User can outsource their vast computational work to the cloud and use massive computational power, storage, software, network etc. Despite all these benefits there are still few obstacles in cloud computing regarding confidentiality and integrity of data. Outsourcing and computation compromises the security of data being stored on cloud computing. Considering cloud as insecure platform a system must be designed that protects data by encryption and as well as produces correct result without any cheating resilience with the help of result verification. In this paper, we study the secure outsourcing and computation of linear programming by capturing the effects of arguments which are of first order and that provides practical efficiency. To achieve efficiency linear programming conditions are implemented. The LP computation are done explicitly decomposing LP problem that are run on cloud. The parameters of LPare owned by the customer. For validating the obtained output of computation, we use duality theorem of linear programming that derives the required condition that the result must fulfil.
Secure and Practical Outsourcing of Linear Programming in Cloud Computing
Cloud computing enables customers with limited computational resources to outsource large-scale computational tasks to the cloud, where massive computational power can be easily utilized in a pay-per-use manner. However, security is the major concern that prevents the wide adoption of computation outsourcing in the cloud, especially when end-user's confidential data are processed and produced during the computation. Thus, secure outsourcing mechanisms are in great need to not only protect sensitive information by enabling computations with encrypted data, but also protect customers from malicious behaviors by validating the computation result. Such a mechanism of general secure computation outsourcing was recently shown to be feasible in theory, but to design mechanisms that are practically efficient remains a very challenging problem.
Secured Auditing of Outsourced Data using Linear Programming in Cloud
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Cloud computing can be seen as an innovation in different ways. From a technological perspective it is an advancement of computing, which’s history can be traced back to the construction of the calculating machine. Cloud Computing has great potential of providing robust computational power to the society at reduced cost. It enables customers with limited computational resources to outsource their large computation workloads to the cloud, and economically enjoy the massive computational power, bandwidth, storage, and even appropriate software that can be shared in a pay-per-use manner. We must design mechanisms that not only protect sensitive information by enabling computations with encrypted data, but also protect customers from malicious behaviors by enabling the validation of the computation result. 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 paramete...
Realistic and Safe Outsourcing of Linear Programming in Cloud Computing
Abstract— Cloud computing makes customers to outsource large-scale computational tasks to the cloud, where massive computational power can be easily utilized in a pay-per-use manner with limited computational resources. However, security is the major concern especially when end-user's confidential data are processed and produced during the computation. Thus,there must be a mechanism which not only protect sensitive information by enabling computations with encrypted data, but also protect customers from malicious behaviors by validating the computation result.To achieve realistic 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. The resulting flexibility allows us to explore appropriate security/efficiency tradeoff via higher-level abstraction of LP computations than the general circuit representation.To validate the computation result, we further explore the fundamental duality theorem of LP computation and derive the necessary and sufficient conditions that correct result must satisfy.
New Algorithms for Secure Outsourcing of Large-Scale Systems of Linear Equations
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Cloud computing is the on-request accessibility of computer system resources, specially data storage and computing power, without direct dynamic management by the client. In the simplest terms, cloud computing means storing and accessing data and programs over the Internet instead of your computer’s hard drive. Along the improvement of cloud computing, more and more applications are migrated into the cloud. A significant element of distributed computing is pay-more only as costs arise. Distributed computing gives strong computational capacity to the general public at diminished cost that empowers clients with least computational assets to redistribute their huge calculation outstanding burdens to the cloud, and monetarily appreciate the monstrous computational force, transmission capacity, stockpiling, and even reasonable programming that can be partaken in a compensation for each utilization way Tremendous bit of leeway is the essential objective that forestalls the wide scope of r...
Secured Auditing of Outsourced Data using L inear Programming in Cloud ijcsit
Cloud computing can be seen as an innovation in different ways. From a technological perspective it is an advancement of computing, which's history can be traced back to the construction of the calculating machine. Cloud Computing has great potential of providing robust computational power to the society at reduced cost. It enables customers with limited computational resources to outsource their large computation workloads to the cloud, and economically enjoy the massive computational po wer, bandwidth, storage, and even appropriate software that can be shared in a pay-per-use manner. We must design mechanisms that not only protect sensitive information by enabling computations with encrypted data, but also protect custome rs from malicious behaviors by enabling the validation of the computation result. 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. The resulting flexibility allo ws us to explore appropriate security/efficiency tradeoff via higher-level abstraction of LP computations than the general circuit representation. In particular, by formulating private data owned by the customer for LP problem as a set of matrices and vectors, we are able to develop a set of efficient privacy-preserving problem transformation techniques, which allow customers to transform original LP problem into some arbitrary one while protecting sensitive input/output info rmation. To validate the computation result, we further explore the fundamental duality theorem of LP computation and derive the necessary and sufficient conditions that correct result must satisfy. Such result verification mechanism is extremely efficient and incurs close-to-zero additional cost on both cloud server and customers.
Practical Secure Computation Outsourcing
ACM Computing Surveys, 2019
The rapid development of cloud computing promotes a wide deployment of data and computation outsourcing to cloud service providers by resource-limited entities. Based on a pay-per-use model, a client without enough computational power can easily outsource large-scale computational tasks to a cloud. Nonetheless, the issue of security and privacy becomes a major concern when the customer’s sensitive or confidential data is not processed in a fully trusted cloud environment. Recently, a number of publications have been proposed to investigate and design specific secure outsourcing schemes for different computational tasks. The aim of this survey is to systemize and present the cutting-edge technologies in this area. It starts by presenting security threats and requirements, followed with other factors that should be considered when constructing secure computation outsourcing schemes. In an organized way, we then dwell on the existing secure outsourcing solutions to different computatio...
IJERT-Outsourcing of Computations in Cloud Computing
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/outsourcing-of-computations-in-cloud-computing https://www.ijert.org/research/outsourcing-of-computations-in-cloud-computing-IJERTV1IS6328.pdf Due to the availability of massive and scalable computational power economically, the emerging cloud computing paradigm has been attractive to the customers with limited computational resources to outsource their large computation workloads. However, security and privacy concerns are majorly obstructing the widespread adoption of this promising computing model especially when the confidential data of the customers is consumed and produced during the computations in the cloud. Devising a mechanism for general secure computation outsourcing was so far theoretically feasible and designing mechanisms that are practically efficient remains a very challenging problem. Focusing on engineering computing and optimization tasks, Cong Wang et al. developed a scheme for secure outsourcing of widely applicable linear programming (LP) computations in the cloud. Also, several works have discussed the outsourcing of nonlinear programming (NLP) computations. In this work we are intended to study and thoroughly analyse both LP and NLP computation outsourcing. Our experimental results show that, due to the complex computations involved, NLP computations consume more time, but, secure than the LP computations outsourcing comparatively.
Cloud computing has become ubiquitous, offers an economical solution for convenient on-demand access to computing resources, which enable the resource-constrained clients to execute extensive computation. However, outsourcing of data and computation to the cloud server is a great cause of concern, such as confidentiality of input/output and verifiability of the result. This paper addresses the problem of designing outsourcing algorithm for linear regression analysis (LR), which is an important data analysis technique and widely applied across multiple domains. The outsourcing framework illustrated by the following scenario: a client is having a large dataset and needs to perform regression analysis, but unable to process due to lack of computing resources. Therefore, the client outsources the computation to the cloud server. In the proposed LR outsourcing algorithm, the client outsources LR problem to the cloud server without revealing to them either the input dataset and the output. The algorithm is a non-interactive solution to the client, it sends only input and receives output along with the proof of verification from the cloud server. The client in the proposed algorithm able to verify the correctness of result with an optimal probability. The analytical analysis shows that the algorithm is successfully meeting the challenges of correctness, security, verifiability, and efficiency. The experimental evaluation validates the proposed algorithm. The result analysis shows that the algorithm is highly efficient and endorses the practical usability of the algorithm.