  Secure Multi-Party Computation (SMC) allows parties with similar background
to compute results upon their private data, minimizing the threat of
disclosure. The exponential increase in sensitive data that needs to be passed
upon networked computers and the stupendous growth of internet has precipitated
vast opportunities for cooperative computation, where parties come together to
facilitate computations and draw out conclusions that are mutually beneficial;
at the same time aspiring to keep their private data secure. These computations
are generally required to be done between competitors, who are obviously weary
of each-others intentions. SMC caters not only to the needs of such parties but
also provides plausible solutions to individual organizations for problems like
privacy-preserving database query, privacy-preserving scientific computations,
privacy-preserving intrusion detection and privacy-preserving data mining. This
paper is an extension to a previously proposed protocol Encrytpo_Random, which
presented a plain sailing yet effective approach to SMC and also put forward an
aptly crafted architecture, whereby such an efficient protocol, involving the
parties that have come forward for joint-computations and the third party who
undertakes such computations, can be developed. Through this extended work an
attempt has been made to further strengthen the existing protocol thus paving
the way for a more secure multi-party computational process.
