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 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. Such an ambitious task requires the collaboration of geographically distributed industries, national laboratories, and universities. Those institutions (including potentially competing industry partners) need to share their private data for building data analysis models to understand the underlying physical phenomena. Omniscient data source eases misuse, such as the growing problem of identity theft. To prevent misuse of data, there is a recent surge in laws mandating protection of confidential data, such as the European Community privacy standards. This protection comes with a real cost through both added security expenditure and penalties and costs associated with disclosure. This would maintain the security provided by separation of control while still obtaining the benefits of a global data source. 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. Although SMC protocols guarantee that nothing other than the final data analysis result is revealed, it is impossible to verify whether or not participating parties are truthful about their private input data. Current SMC techniques cannot prevent input modification by participating parties.