MOZAIK: A Privacy-Preserving Analytics Platform for IoT Data Using MPC and FHE

TL;DR

MOZAIK is a new privacy-preserving IoT data platform that uses advanced cryptographic techniques like MPC and FHE to keep data encrypted during storage and processing, enhancing security in cloud-based IoT systems.

Contribution

It introduces MOZAIK, an end-to-end architecture combining MPC and FHE for secure IoT data analytics, with a proof-of-concept and open-source release.

Findings

Feasibility demonstrated through implementation and performance evaluation.

Encryption overhead is manageable for practical IoT analytics.

Open-source components facilitate adoption and further research.

Abstract

The rapid increase of Internet of Things (IoT) systems across several domains has led to the generation of vast volumes of sensitive data, presenting significant challenges in terms of storage and data analytics. Cloud-assisted IoT solutions offer storage, scalability, and computational resources, but introduce new security and privacy risks that conventional trust-based approaches fail to adequately mitigate. To address these challenges, this paper presents MOZAIK, a novel end-to-end privacy-preserving confidential data storage and distributed processing architecture tailored for IoT-to-cloud scenarios. MOZAIK ensures that data remains encrypted throughout its lifecycle, including during transmission, storage, and processing. This is achieved by employing a cryptographic privacy-enhancing technology known as computing on encrypted data (COED). Two distinct COED techniques are explored, specifically secure multi-party computation (MPC) and fully homomorphic encryption (FHE). The paper includes a comprehensive analysis of the MOZAIK architecture, including a proof-of-concept implementation and performance evaluations. The evaluation results demonstrate the feasibility of the MOZAIK system and indicate the cost of an end-to-end privacy-preserving system compared to regular plaintext alternatives. All components of the MOZAIK platform are released as open-source software alongside this publication, with the aim of advancing secure and privacy-preserving data processing practices.