Cloud computing paradigm offers customers instant and affordable access to a distributed pool of computational resources for solving large complex problems.  However, customers are still wary of sending their problems to public cloud infrastructure due to security concerns. One of their major worries is that key proprietary design information about their application will be comprised. In this talk, I will discuss an approach for obfuscating critical design information when solving large optimization problems in the cloud. <?xml:namespace prefix = o />

In our approach, the customer first models his/her application as an optimization problem. Key design information in the optimization problem is then masked using certain optimality-preserving transformations. The masked optimization problem is sent to the cloud and its best solution is returned.  The customer re-transforms the cloud's solution to obtain the desired solution to the original problem.  The challenges are to ensure that: (i) the cloud cannot reconstruct key design information from the masked problem and, (ii) the final answer is best solution to the original problem.

In this talk, I will illustrate our approach using two optimization problems, one from the area of electronic design automation and the other from power engineering.