Malay Kumar

Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint

Cloud computing is becoming an increasingly admired paradigm that delivers high-performance computing resources over the Internet to solve the large-scale scientific problems, but still it has various challenges that need to be addressed to execute scientific workflows. The existing research mainly focused on minimizing finishing time (makespan) or minimization of cost while meeting the quality of service requirements. However, most of them do not consider essential characteristic of cloud and major issues, such as virtual machines (VMs) performance variation and acquisition delay. In this paper, we propose a meta-heuristic cost effective genetic algorithm that minimizes the execution cost of the workflow while meeting the deadline in cloud computing environment. We develop novel schemes for encoding, population initialization, crossover, and mutations operators of genetic algorithm. Our proposal considers all the essential characteristics of the cloud as well as VM performance variation and acquisition delay. Performance evaluation on some well-known scientific workflows, such as Montage, LIGO, CyberShake, and Epigenomics of different size exhibits that our proposed algorithm performs better than the current state-of-the-art algorithms.

Privacy preserving, verifiable and efficient outsourcing algorithm for regression analysis to a malicious cloud

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.

Privacy preserving, verifiable and efficient outsourcing algorithm for matrix multiplication to a malicious cloud server

Abstract Matrix Multiplication is a basic engineering and scientific problem, which has application in various domains. There exists many cryptographic solutions for secure computation of matrix multiplication, but cryptographic preamble makes them infeasible for outsourcing with large input size to the cloud server. In this paper, we propose a privacy-preserving, verifiable and efficient algorithm for matrix multiplication in outsourcing paradigm illustrated by the following scenario: the client is having a large data-set and needs to perform matrix multiplication, but unable to process due to the lack of computing resources. Therefore, the client outsources the computation to the cloud server. We evaluate the algorithm on security, efficiency and verifiability parameters and discuss the implementation details. The result analysis shows that the algorithm is highly efficient and endorses the practical usability of the algorithm. Using this algorithm, we can mostly replace the costly cryptographic operations and securely solve matrix multiplication on a large data-set.

Secure & efficient delegation of system of linear equation to a malicious cloud server

Cloud computing offers an economical solution to the computationally weak clients. It enables the client to execute large computations beyond their computation capacity by outsourcing their computation load to the massive cloud servers. However, outsourcing of data and the computation to a third-party cloud server bring many security and privacy concern. In this paper, we are addressing the problem of system of linear equation (LE), which is a basic engineering and scientific problem that has application in various areas. We are employing efficient linear transformation technique using the concepts of linear algebra. The proposed algorithm transform the dataset to some other form to hide the original dataset. The algorithm provides a non-interactive proof of solution therefore, the algorithm executes with an optimal one round of communication. The proposed algorithm also employs an efficient result verification method to perceive deceitful server behavior with an optimal probability, thus preserving the data integrity. The result verification method is very efficient and run with modest overhead. The proposal has been verified through theoretical and experimental analysis to demonstrate the practical usability of the proposed algorithm.

Secure and Efficient Regression Analysis Outsourcing Algorithm for Cloud Server

IT industry has been experiencing the benefits of outsourcing for years, since the outsourcing brings down both the capital and operational expenditure. Similarly, cloud computing provides storage, computation, and other specialized services on demand to customers over the internet at a very generous cost. However, outsourcing data and computation to a third party cloud server is a great cause of concern to the client because a customer physically loses control over their sensitive/classified data and computation. The loss of physical control over the data and computation is the main issue for a client that makes him feel insecure using cloud computing services. The solution to address outsourcing issues, first, the cloud service provider must be honest by providing correct and secure computation; second, the outsourced data and computation shall be verifiable to customers in terms of confidentiality and integrity. In this paper, we investigate the problem of regression analysis, outsourcing problem and devise a secure and efficient outsourcing algorithm which provides security for the input and also provide safeguards to the output result computed on cloud servers. Further, a novel and efficient result verification technique have been developed to detect server misbehavior and cheating with optimal probability of 1. Furthermore, theoretical and experimental analysis has compared with existing algorithm to demonstrate the efficiency, security and effectiveness of the proposed algorithms.

Public delegation & verifiability of matrix multiplication to a malicious cloud server

The rapid development of cloud computing services and expansion of mobile computing devices have made computation outsourcing a promising solution for execution of extensive computation. In this framework, a computationally weak client outsources its large computation load to a cloud server. However, outsourcing of data and computation to a cloud server brings many security and privacy concern. In this paper, we are addressing matrix multiplication (MM) problem, because MM is a computation-intensive problem and useful in many domains. In the proposed MM outsourcing algorithm, the client outsources input dataset to the cloud server without revealing to them both 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. Further, this work extends the definition of verifiable computation to public verifiable computation, which allows participating worker (not only the client) to verify the correctness of the result computed on the cloud server. The analytical analysis shows that the algorithm is successfully meeting the challenges of correctness, security, verifiability, and efficiency. The practical evaluation validates the proposed algorithm. The result analysis shows that the algorithm is highly efficient and endorses the practical usability of the algorithm.