Multi-Party Computation in IoT for Privacy-Preservation

TL;DR

This paper proposes an efficient Multi-Party Computation strategy using Shamir's Secret Sharing tailored for resource-constrained IoT systems to enhance privacy-preserving data aggregation.

Contribution

It introduces an optimized MPC approach based on concurrent-transmission technology, suitable for real-world IoT applications, overcoming limitations of existing methods.

Findings

Achieved efficient privacy-preserving data aggregation in IoT systems.

Reduced computational and communication overhead compared to traditional methods.

Demonstrated practical applicability in resource-constrained environments.

Abstract

Preservation of privacy has been a serious concern with the increasing use of IoT-assisted smart systems and their ubiquitous smart sensors. To solve the issue, the smart systems are being trained to depend more on aggregated data instead of directly using raw data. However, most of the existing strategies for privacy-preserving data aggregation, either depend on computation-intensive Homomorphic Encryption based operations or communication-intensive collaborative mechanisms. Unfortunately, none of the approaches are directly suitable for a resource-constrained IoT system. In this work, we leverage the concurrent-transmission-based communication technology to efficiently realize a Multi-Party Computation (MPC) based strategy, the well-known Shamir's Secret Sharing (SSS), and optimize the same to make it suitable for real-world IoT systems.