The secure multiparty computation is the subfield of the cryptography for the parties to jointly compute the function on their inputs while keeping the private input with the goal of the creation method. It is also known as multi-party computation, secure computation, or privacy-preserving computation. Furthermore, the secure multiparty computation is the cryptographic protocol which is used to distribute the computation throughout multiple parties where any person or party cannot see the data of the other parties using the networks (Bogetoft, 2009).

Furthermore, the protocols of secure multiparty computation can enable the analysts and data scientist to compute data securely, compliantly as well as privately, distributed without ever moving or exposing. The secret computing technology is encompassing two encryption complementary techniques in use. There are several very important benefits or goals of the secure multiparty computing discussed in this document. The mentioned aspects of the secure multiparty computation which are provided in this document are providing information about how important the security multiparty computation is. Some important points are mentioned and their important description and detail of everything are provided (Lindell, 2005).

Make ready for Use Commercially: The main security multiparty computation is the use of the protocol commercially. The secure multiparty computation is no longer the dream of the data scientist such as it proves the reality. The secure multiparty computation is being used in production nowadays by checking out the page of use cases (Goldreich, 1998).

Unauthorized and Untrusted their parties cannot see the data

It is very important in any network protocol that any third person or any unauthorized person is will be restricted to access the data of other persons as well as no any untrusted party will be allowed to see any kind of data in of any user. Although, it is no more essential to trust any third party to keep the information safe as well as the broker exchanges. Thus, the data outside their internal firewalls are never be transferred by the clients (Ben-David, et al., 2008).

Excluding tradeoff among data privacy and the data usability

There is no need to drop or mask any kind of characteristics in the sense to preserve data privacy. It may use all of the features of the protocols in the analysis without compromising the security of the data or the privacy of the information and without compromising the confidentiality of the party’s data (Cramer, et al., 2000).

High precision and Accuracy

For the precision and accuracy, the results meet as well as exceed the requirements of the client. So, it is also another security goal of secure multiparty computation (Du & Atallah, 2001).

Quantum-Safe: The data is said to be encrypted in use because it is broken up as well as split throughout the players during the computation. Moreover, the quantum-safe makes the data safe against the attacks of quantum (Ishai, et al., 2007).

Sovereign data privacy compliance and GDPR

The secure multiparty computation solution exceeds as well as meets with the requirements for data transfer border cross.

Conclusion of the Secure Multiparty Computation

In the conclusion of this article, the important aspects are highlighted. First of all, secure multipart computing is the branch of cryptography in which different parties such as sender and receiver compute the information jointly while keeping private input. It is also concluded that it also refers to the cryptographic protocol and by using this, no party can see data of other parties. To explain the secure multiparty computing, some important points are included in document.

References of the Secure Multiparty Computation

Ben-David, A., Nisan, N. & Pinkas, B., 2008. FairplayMP: a system for secure multi-party computation. In Proceedings of the 15th ACM conference on Computer and communications security, pp. 257-266..

Bogetoft, P., 2009. Secure multiparty computation goes live. In International Conference on Financial Cryptography and Data Security, pp. 325-343.

Cramer, R., Damgård, I. & Maurer, U., 2000. General secure multi-party computation from any linear secret-sharing scheme. In International Conference on the Theory and Applications of Cryptographic Techniques, pp. 316-334..

Du, W. & Atallah, M. J., 2001. Secure multi-party computation problems and their applications: a review and open problems. In Proceedings of the 2001 workshop on New security paradigms, pp. 13-22..

Goldreich, O., 1998. Secure multi-party computation. Manuscript. Preliminary version, p. 78..

Ishai, Y., Kushilevitz, E., Ostrovsky, R. & Sahai, A., 2007. Zero-knowledge from secure multiparty computation. In Proceedings of the thirty-ninth annual ACM symposium on Theory of computing, pp. 21-30..

Lindell, Y., 2005. Secure multiparty computation for privacy preserving data mining. In Encyclopedia of Data Warehousing and Mining, pp. 1005-1009..