In the era of Big Data and the advancement of the Internet of Things, there is an increasing amount of valuable information. It is important to emphasize that this data is usually sensitive or confidential, so security and privacy are two of the highest priorities for organizations when performing Data Mining. Researchers have explored techniques such as secure multi-party computation (SMPC) in the last decades. Nevertheless, there is still a significant gap between the theory of SMPC and its applicability, especially when working with resource-constrained devices or massive data. This work has been conducted with a systematic literature review, and it intends to analyze the open issues of adapting SMPC to those scenarios, by classifying the studies to answer two research questions: (1) how has the use of SMPC attempted to be adapted to constrained devices? and (2) how have traditional techniques fitted with Big Data? At the end of the process, after analyzing a total of 637 studies, 19 papers were selected. Regarding constrained devices, solutions are grouped into three main techniques: secure outsourcing, hardware-based trusted execution, and intermediate representations. As for Big Data, the selected studies use mixed protocols to change over cleartext and ciphertext, combine different types of SMPC protocols, or modify existing protocols through optimizations.

Homomorphic Encryption plays a key role in secure multi-source data aggregation because it enables computations to be performed directly over encrypted data, allowing distributed parties to contribute sensitive information while preserving confidentiality. However, its use in this context introduces three main challenges: existing approaches often focus on specific operations rather than supporting diverse analytics across multiple encrypted data sources; key generation and management frequently rely on trusted third parties or require private keys to be shared; and TEE-based solutions may avoid trusted third parties but often require computations to be partially executed inside the trusted environment, thereby limiting deployment flexibility. To address these limitations, this work makes three main contributions: (i) the proposal of a complete framework for secure multi-source data aggregation that leverages homomorphic encryption and enables any data consumer to securely run multi-source data aggregations over data previously registered by untrusted data providers; (ii) the integration of secure enclaves for the secure generation, distribution, and management of homomorphic keys, addressing challenges related to coordinated key synchronization in multi-party aggregation environments and removing the need for a trusted third party; and (iii) the introduction of a hybrid key management protocol that combines secure enclave-based key generation with efficient key distribution and secure aggregation outside the enclave, in the data consumer, minimizing trust assumptions and computational overhead. The implementation and evaluation on small-scale aggregated datasets show that the proposed approach effectively addresses the identified challenges by providing, to the best of the authors’ knowledge, the first practical and privacy-preserving solution that supports different algorithms without relying on any trusted third party, while improving over existing solutions through the integration of secure enclaves and a hybrid key management protocol.

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. La privacidad es un sector en auge y cada vez son más las limitaciones que dificultan la centralización de datos procedentes de diferentes fuentes. Hoy en día, disponer de datos aporta un valor y una ventaja sobre el resto, ya que permite realizar un análisis más amplio y generalizable. La Computación Segura Multiparte (SMPC) es una técnica criptográfica que permite realizar cálculos con datos de diferentes partes manteniendo la privacidad de los mismos y evitando la centralización. Este trabajo se centra en la herramienta de SCALE-MAMBA para llevar a cabo SMPC y el objetivo principal es su validación en cuanto a tipos de operaciones, precisión de los resultados y tiempos de ejecución. Se utiliza un caso de uso directamente relacionado con la industria, que consiste en un fabricante que desea implementar mantenimiento predictivo sobre una máquina cuyos datos son recogidos por diferentes usuarios. Se presentan dos tipos de escenarios para analizar los resultados, obteniendo diferentes conclusiones para cada uno de ellos. Por un lado, el primer escenario recoge los casos de uso en los que se pretende realizar estadísticas o cálculos sencillos con datos en común. Por otro lado, el segundo escenario se centra en el entrenamiento de algoritmos de aprendizaje automático (ML). La aportación original de este trabajo incluye la implementación de estos códigos en el lenguaje Mamba, su aplicación a datos concretos y la comparación de los resultados con los que se obtendrían realizándolo de forma insegura, centralizando los datos y utilizando R o Python. Las principales limitaciones encontradas giran en torno a los tiempos de ejecución, que podrían ser aceptables para muchos casos de uso en el primer escenario, pero que resultan prohibitivos para muchas de las técnicas utilizadas en los entrenamientos reales de ML. Palabras clave: criptografía, seguridad, privacidad, mantenimiento predictivo, computación para preservar la privacidad, tecnologías de mejora de privacidad, computación segura multi-parte, SCALE-MAMBA, aprendizaje automático, análisis de datos, predicción, clasificación, precisión, eficiencia.