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

ABSTRACT:

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.

EXISTING SYSTEM:

• Recent researches in both the cryptography and the theoretical computer science communities have made steady advances in “secure outsourcing expensive computations”.
• Based on Yao’s garbled circuits and Gentry’s breakthrough work on fully homomorphic encryption (FHE) scheme, a general result of secure computation outsourcing has been shown viable in theory, where the computation is represented by an encrypted combinational boolean circuit that allows to be evaluated with encrypted private inputs.
• Frikken give a provably secure protocol for secure outsourcing matrix multiplications based on secret sharing. While this work outperforms their previous work in the sense of single server assumption and computation efficiency (no expensive cryptographic primitives), the drawback is the large communication overhead. Namely, due to secret sharing technique, all scalar operations in original matrix multiplication are expanded to polynomials, introducing significant amount of overhead.

DISADVANTAGES OF EXISTING SYSTEM:

• 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.
• In existing approaches, either heavy cloud-side cryptographic computations or multi-round interactive protocol executions, or huge communication complexities, are involved.
• In short, practically efficient mechanisms with immediate practices for secure computation outsourcing in cloud are still missing.

PROPOSED SYSTEM:

• In this paper, we study practically efficient mechanisms for secure outsourcing of linear programming (LP) computations. Linear programming is an algorithmic and computational tool which captures the first order effects of various system parameters that should be optimized, and is essential to engineering optimization.
• We propose to explicitly decompose the LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer.
• Specifically, we first formulate private data owned by the customer for LP problem as a set of matrices and vectors. This higher level representation allows us to apply a set of efficient privacy-preserving problem transformation techniques, including matrix multiplication and affine mapping, to transform the original LP problem into some random one while protecting the sensitive input/output information.

ADVANTAGES OF PROPOSED SYSTEM:

• It has been widely used in various engineering disciplines that analyze and optimize real-world systems/models, such as packet routing, flow control, power management of data centers, etc.
• The flexibility of such decomposition allows us to explore higher level abstraction of LP computations than the general circuit representation for the practical efficiency.
• For the first time, we formalize the problem of securely outsourcing LP computations, and provide such a secure and practical mechanism design which fulfills input/output privacy, cheating resilience, and efficiency.
• Our mechanism brings cloud customer great computation savings from secure LP outsourcing as it only incurs overhead on the customer, while solving a normal LP problem usually requires more time.
• The computations done by the cloud server shares the same time complexity of currently practical algorithms for solving the linear programming problems, which ensures that the use of cloud is economically viable.
• The experiment demonstrates the immediate practicality: our mechanism can always help customers achieve more than 50% savings when the sizes of the original LP problems (with feasible solutions) are not too small, while introducing no substantial overhead on the cloud.

SYSTEM ARCHITECTURE:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System                           :         Pentium Dual Core.

• Hard Disk                      :         120 GB.

• Monitor                         :         15’’ LED

• Input Devices                 :         Keyboard, Mouse

• Ram                               :         1GB.

SOFTWARE REQUIREMENTS: 

• Operating system                    :         Windows 7.
• Coding Language           :         JAVA/J2EE
• Tool                               :         Netbeans 7.2.1
• Database                        :         MYSQL

REFERENCE:

Cong Wang, Member, IEEE, Kui Ren, Senior Member, IEEE, and Jia Wang, Member, IEEE, “Secure Optimization Computation Outsourcing in Cloud Computing: A Case Study of Linear Programming”, IEEE TRANSACTIONS ON COMPUTERS, VOL. 65, NO. 1, JANUARY 2016.