Secure Optimization Computation Outsourcing in Cloud Computing: A Case Study of Linear Programming

0
388
Secure Optimization Computation Outsourcing in Cloud Computing: A Case Study of Linear Programming

Secure Optimization Computation Outsourcing in Cloud Computing: A Case Study of Linear Programming

Abstract

Secure Optimization Computation Outsourcing in Cloud Computing: A Case Study of Linear Programming,Cloud computing enables an economically promising paradigm of computation outsourcing. However, how to protect customers confidential data processed and generated during the computation is becoming the major security concern. Focusing on engineering computing and optimization tasks, this paper investigates secure outsourcing of widely applicable linear programming (LP) computations. Our mechanism design explicitly decomposes 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 computation than the general circuit representation. Specifically, by formulating private LP problem as a set of matrices/vectors, we develop efficient privacy-preserving problem transformation techniques, which allow customers to transform the original LP into some random one while protecting sensitive input/output information. To validate the computation result, we further explore the fundamental duality theorem of LP and derive the necessary and sufficient conditions that correct results must satisfy. Such result verification mechanism is very efficient and incurs close-to-zero additional cost on both cloud server and customers. Extensive security analysis and experiment results show the immediate practicability of our mechanism design.
 

Advantages

1. Price: Pay for only the resources used.
2.Security: Cloud instances are isolated in the network from other instances for improved security.
3.Performance: Instances can be added instantly for improved performance. Clients have access to the total resources of the Cloud’s core hardware.
4. Scalability: Auto-deploy cloud instances when needed.
5. Uptime: Uses multiple servers for maximum redundancies. In case of server failure, instances can be automatically created on another server. 6. Control: Able to login from any location. Server snapshot and a software library lets you deploy custom instances.
7. Traffic: Deals with spike in traffic with quick deployment of additional instances to handle the load.
 

Disadvantages

  1. Applying the existing mechanism to our daily computations would be far from practical, due to the extremely high complexity of FHE operation as well as the pessimistic circuit sizes that cannot be handled in practice when constructing original and encrypted circuits.
  2. In existing approaches, either heavy cloud-side cryptographic computations or multiround interactive protocol executions, or huge communication complexities, are involved.
  3. In short, practically efficient mechanisms with immediate practices for secure computation outsourcing in cloud are still missing.
 
 

System Configuration

H/W System Configuration
Speed                   : 1.1 GHz
 
RAM                      : 256 MB(min)
 
Hard Disk              : 20 GB
 
Floppy Drive          : 1.44 MB
 
Key Board             : Standard Windows Keyboard
 
Mouse                  : Two or Three Button Mouse
 
Monitor                : SVGA
 
S/W System Configuration
 
 
Platform                     :  cloud computing

 
Operating system       : Windows Xp,7,
 
Server                       : WAMP/Apache
 
Working on                : Browser Like Firefox, IE

Conclusion

In this Secure Optimization Computation Outsourcing in Cloud Computing: A Case Study of Linear Programming paper, for the first time, we formalized the problem of securely outsourcing LP computations in cloud computing, and provided such a practical mechanism design which fulfills input/output privacy, cheating resilience, and efficiency. By explicitly decomposing LP computation outsourcing into public LP solvers and private data, our mechanism design is able to explore appropriate security/efficiency tradeoffs via higher level LP computation than the general circuit representation. We developed problem transformation techniques that enable customers to secretly transform the original LP into some random one while protecting sensitive input/output information. We also investigated duality theorem and derived a set of necessary and sufficient condition for result verification. Such a cheating resilience design can be bundled in the overall mechanism with close-to-zero additional overhead. Both security analysis and experiment results demonstrates the immediate practicality of the proposed mechanism.