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New Algorithms for Secure Outsourcing of Large-Scale Systems of
Linear Equations
Abstract:
With the rapid development in availability of cloud services, the
techniques for securely outsourcing the prohibitively expensive
computations to untrusted servers are getting more and more attentions in
the scientific community. In this paper, we investigate secure outsourcing
for large-scale systems of linear equations, which are the most popular
problems in various engineering disciplines. For the first time, we utilize
the sparse matrix to propose a new secure outsourcing algorithm of large-
scale linear equations in the fully malicious model. Compared with the
state-of-the-art algorithm, the proposed algorithm only requires (optimal)
one round communication (while the algorithm requires L rounds of
interactions between the client and cloud server, where L denotes the
number of iteration in iterative methods). Furthermore, the client in our
algorithm can detect the misbehavior of cloud server with the (optimal)
probability 1. Therefore, our proposed algorithm is superior in both
efficiency and checkability. We also provide the experimental evaluation
that demonstrates the efficiency and effectiveness of our algorithm.
Existing System:
Cloud computing has plenty of benefits for real-world applications such as
on-demand self-service, ubiquitous network access, location independent
resource pooling, rapid resource elasticity, usage based pricing,
outsourcing, etc. In the outsourcing computation paradigm, the users with
resource-constraint devices can outsource heavy computation workloads
into the cloud server and enjoy the unlimited computing resources in a
pay-peruse manner. As a result, the enterprises and individuals can avoid
large capital outlays in hardware/software deployment and maintenance.
Proposed System:
We propose a new secure outsourcing algorithm for large-scale systems of
linear equations Ax = b. Our proposed algorithm works with a single cloud
server and the server is assumed to be lazy, curious, and dishonest (fully
malicious model). Compared with the state-of-the-art algorithm [48], the
proposed algorithm is superior in both efficiency and check ability.
Our contributions are three folds:
1) For the first time, we utilize the sparse matrix to investigate securely
outsourcing for large-scale systems of linear equations. Our algorithm is
suitable for any nonsingular dense matrix A. However, in algorithm, A
must be a strictly diagonally dominant matrix for convergence.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.
• Front End : - .Net
• Back End : - SQL Server

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New algorithms for secure outsourcing of large scale systems of linear equations

  • 1. New Algorithms for Secure Outsourcing of Large-Scale Systems of Linear Equations Abstract: With the rapid development in availability of cloud services, the techniques for securely outsourcing the prohibitively expensive computations to untrusted servers are getting more and more attentions in the scientific community. In this paper, we investigate secure outsourcing for large-scale systems of linear equations, which are the most popular problems in various engineering disciplines. For the first time, we utilize the sparse matrix to propose a new secure outsourcing algorithm of large- scale linear equations in the fully malicious model. Compared with the state-of-the-art algorithm, the proposed algorithm only requires (optimal) one round communication (while the algorithm requires L rounds of interactions between the client and cloud server, where L denotes the number of iteration in iterative methods). Furthermore, the client in our algorithm can detect the misbehavior of cloud server with the (optimal) probability 1. Therefore, our proposed algorithm is superior in both efficiency and checkability. We also provide the experimental evaluation that demonstrates the efficiency and effectiveness of our algorithm. Existing System:
  • 2. Cloud computing has plenty of benefits for real-world applications such as on-demand self-service, ubiquitous network access, location independent resource pooling, rapid resource elasticity, usage based pricing, outsourcing, etc. In the outsourcing computation paradigm, the users with resource-constraint devices can outsource heavy computation workloads into the cloud server and enjoy the unlimited computing resources in a pay-peruse manner. As a result, the enterprises and individuals can avoid large capital outlays in hardware/software deployment and maintenance. Proposed System: We propose a new secure outsourcing algorithm for large-scale systems of linear equations Ax = b. Our proposed algorithm works with a single cloud server and the server is assumed to be lazy, curious, and dishonest (fully malicious model). Compared with the state-of-the-art algorithm [48], the proposed algorithm is superior in both efficiency and check ability. Our contributions are three folds: 1) For the first time, we utilize the sparse matrix to investigate securely outsourcing for large-scale systems of linear equations. Our algorithm is suitable for any nonsingular dense matrix A. However, in algorithm, A must be a strictly diagonally dominant matrix for convergence. Hardware Requirements: • System : Pentium IV 2.4 GHz. • Hard Disk : 40 GB. • Floppy Drive : 1.44 Mb. • Monitor : 15 VGA Colour.
  • 3. • Mouse : Logitech. • RAM : 256 Mb. Software Requirements: • Operating system : - Windows XP. • Front End : - JSP • Back End : - SQL Server Software Requirements: • Operating system : - Windows XP. • Front End : - .Net • Back End : - SQL Server