Challenges and future research directions in secure multi-party computation for resource-constrained devices and large-scale computations

Abstract 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.