Secure Multiparty Computation Protocol: Basic Building Blocks Methods

In a modern information-driven society, the everyday life of individuals and companies is full of cases where various kinds of private information is an important resource. While a cryptographer might think of PIN-codes and keys in this context, this type of secrets is not our main concern here. Secure Multiparty Computation (S MC) allows parties to compute the combine result of their individual data without revealing their data to others. S MC also allows parties with similar background to compute results upon their private data, minimizing the threat of disclosure. On S MC many eminent researchers give their protocols especially in modifier tokens to compute result. As the data involved in computation was encrypted, without revealing the data right result can be computed and privacy of the parties is maintained. In this paper, we survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacy-preserving data mining. In addition to reviewing definitions and constructions for secure multiparty computation, we discuss the issue of efficiency and demonstrate the difficulties involved in constructing highly efficient protocols. Finally, we discuss the relationship between secure multiparty computation and privacy-preserving data mining, and show which problems it solves and which problems it does not secure sum computation, researchers show their interest. The process involves encrypting data in a manner that it does not affect the result of the computation. Virtual parties are created by all organizations and encrypted data is distributed among them. Modifier tokens are generated along encryption which are assigned to virtual parties, and finally used in the computation. The computation function uses the acquired data and are responsible for generating the basic methods to be used for extraction of the given set of data.