Privacy-Preserving Big Data Analytics: From Theory to Practice

Abstract

In the last decade, with the advent of Internet of Things (IoT) and Big Data phenomenons, data security and privacy have become very crucial issues. A significant portion of the problem is due to not utilizing appropriate security and privacy measures in data and computational infrastructures. Secure multiparty computation (secure MPC) is a cryptographic tool that can be used to deal with the mentioned problems. This computational approach has attracted increasing attention, and there has been significant amount of advancement in this domain. In this paper, we review the important theoretical bases and practical advancements of secure multiparty computation. In particular, we briefly review three common cryptographic primitives used in secure MPC and highlight the main arithmetic operations that are performed at the core of secure MPC protocols. We also highlight the strengths and weaknesses of different secure MPC approaches as well as the fundamental challenges in this domain. Moreover, we review and compare the state-of-the-art secure MPC tools that can be used for addressing security and privacy challenges in the IoT and big data analytics. Using secure MPC in the IoT and big data domains is a challenging task and requires significant expert knowledge. This technical review aims at instilling in the reader an enhanced understanding of different approaches in applying secure MPC techniques to the IoT and big data analytics.