Privacy-Preserving Computing Protocols that Protect the Inputs of All Participants

This paper focuses on the analysis of cryptographic protocols that allow two or more parties to compute functions of their shared inputs without exposing those inputs in their original form. This is a fundamental element of SMPC. Thanks to the security achieved by protecting data during computations, SMPC allows participants to perform computations while protecting the data. It is used in areas such as finance, healthcare, and data analytics, where sensitive data is involved. The study focuses on approaches for introducing security into the studied protocols to ensure their correctness while preserving user privacy. This is done through the use of tools such as homomorphic encryption, secret sharing, and zero-knowledge technology. We consider different security configurations, such as semi-honest and malicious security, to understand the vulnerability of these protocols to potential attacks or data disclosure. Furthermore, important topics such as scaling and computational complexity are addressed, proposing solutions to minimize communication costs and time in the context of Big Data applications. The result demonstrates that it is possible to implement a secure and practical SMPC with strong security guarantees, regardless of the performance requirements of various real-world application scenarios. This work is relevant to the current state of cryptography and proposes new protocols that enable sensitive computations for real-world applications while preserving privacy in the modern digital environment.