PRIVACY and security, particularly maintaining confidentiality of data, have become a challenging issue with advances in information and communication technology. The ability to communicate and share data has many benefits, and the idea of an omniscient data source carries great value to research and building accurate data analysis models. For example, for credit card companies to build more comprehensive and accurate fraud detection system, credit card transaction data from various companies may be needed to generate better data analysis models. Department of Energy supports research on building much more efficient diesel engines. Secure multiparty computation (SMC) has recently emerged as an answer to this problem. Informally, if a protocol meets the SMC definitions, the participating parties learn only the final result and whatever can be inferred from the final result and their own inputs. A simple example is Yao’s millionaire problem : two millionaires, Alice and Bob, want to learn who is richer without disclosing their actual wealth to each other. Recognizing this, the research community has developed many SMC protocols. SMC, the participating parties provide truthful inputs. This assumption is usually justified by the fact that learning the correct data analysis models or results is in the best interest of all participating parties. Since SMC-based protocols require participating parties to perform expensive computations.