Abstract
Privacy is a booming sector and there is an increasing number of limitations that hinder the centralization of data coming from different sources. Nowadays, having data provides value and an advantage over the rest, since it allows the performance of a wider and more generalizable analysis. Secure Multiparty Computation (SMPC) is a cryptographic technique that allows performing computations with data from different parties while maintaining the privacy of the data and avoiding centralization. This work focuses on the SCALE-MAMBA framework for conducting SMPC and the main objective is its validation in terms of types of operations, the accuracy of the results and execution times. A use case that is directly related to the industry is used, consisting of a manufacturer who wants to implement predictive maintenance on a machine whose data is collected by different users. Two types of scenarios are presented in order to analyze the results, obtaining different conclusions for each of them. On the one hand, the first scenario collects the use cases in which the aim is to compute statistics or simple calculations with data in common. On the other hand, the second scenario focuses on the training of Machine Learning (ML) algorithms. The original contribution of this work includes the implementation of these codes within the Mamba language, their application to concrete data, and the comparison of the results with those that would be obtained by performing it in an insecure way, centralizing the data, and using R or Python. The major limitations encountered are around execution times, which might be acceptable for many use cases in the first scenario, but are prohibitive for many of the techniques used in real ML training.
Keywords: cryptography, security, privacy, predictive maintenance, Privacy-Preserving Computation, Privacy-enhancing technologies, Secure Multiparty Computation, SCALE-MAMBA, machine learning, data analysis, prediction, classification, accuracy, efficiency.