Realistic and Safe Outsourcing of Linear Programming in Cloud Computing (original) (raw)

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.

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 Outsourcing of Linear Programming Solver in Cloud Computing: A Survey

2019

There are currently major concerns about how to safeguard and process the data processed by infection. Innumerable industrial, figuring and optimization methods are being used to resolve this problem. The problem has been fixed for secure outsourcing for large issues. In this paper, the terms required in Cloud Security have been presented. The privacy feats of secure cloud are used to frustrate, to achieve more aspects of security. While cloud computing is being used to outsource large scale computer outsourced to the cloud, data privacy has become a major problem. In this paper, modern cryptographic techniques, which have been sourcing with research work proposed in the previous years. Based on some flaw fixes, the current situation has been identified. There is also the motivation for this paper problem and future research guidelines.

Secured Auditing of Outsourced Data using Linear Programming in Cloud

2012

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...

IJERT-Privacy Assured Delegation of Massive Linear Programming Computational Workload

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/privacy-assured-delegation-of-massive-linear-programming-computational-workload https://www.ijert.org/research/privacy-assured-delegation-of-massive-linear-programming-computational-workload-IJERTV3IS051148.pdf Cloud Computing is a membership based service where you can obtain the networked storage space and computer resources. In this cloud computing model, the customers connect to the cloud to access IT resources which are charged and provided on demand services. This model is composed of five important characteristics, three service models and four deployment models. Users can store their data in the cloud and there is a lot of personal information and secure data that people store on their computers, and this information is now being transferred to the cloud. So we must ensure the security of user's data, which is stored in the cloud. In this paper we present privacy assured delegation mechanism for linear programming computations in the cloud computing environment. Linear programming is a computational tool, which is used to analyze and optimize real world systems. Here we built the privacy assured LP delegation mechanism using a different approach i.e. iterative method which is easy to implement practically and requires only simple matrix-vector operations. In this mechanism customer can keep confidential both input and output of the computation secure by using additive homomorphic encryption method and can use the cloud for iteratively finding successive approximations to the LP solution. For untrusted cloud cheating detection, we use efficient verification mechanism that allows customers to verify all results from cloud effectively.

New Algorithms for Secure Outsourcing of Large-Scale Systems of Linear Equations

2020

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.

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.

Non Linear Programming Computation Outsourcing in the Cloud

2012

Cloud Computing is termed with great potential in providing robust computational power to the society at low cost. This enables customers with limited computational resources to outsource the large computation workloads to the cloud, and to enjoy massive computational power, bandwidth, and storage economically. Connecting the cloud to an intrinsically insecure computing platform from the viewpoint of the cloud customers, must model mechanisms not only protect sensitive information by enabling computations with secured encrypted data, but also protect customers against malicious behaviors by including the validation of the computation result. Such a functionality of general secure computation outsourcing was shown to be feasible in theory recently, but to design mechanisms that are practical efficient remains a very challenging problem. Nonlinear programming problems are in more general difficult than linear programming problems, and often hence the way out found is only a local opti...