An Efficient and Secure Nonlinear Programming Outsourcing in Cloud Computing

Cloud Computing provides a appropriate on-demand network access to a shared pool of configurable computing resources which could be rapidly deployed with much more great efficiency and with minimal overhead to management. This paper deals with the secure outsourcing of nonlinear programming. It provides a practical mechanism design which fulfils input/output privacy, cheating resilience, and efficiency. In the proposed approach practical efficiency is achieved by explicit decomposition of NLP into NLP solvers running on the cloud and private NLP parameters owned by the customer. When compared to the general circuit representation the resulting flexibility allows exploring appropriate security/efficiency trade-off via higher-level abstraction of NLP computations.