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

A Secure Multi-Party Computation Protocol for Malicious Computation Prevention for preserving privacy during Data Mining