SPRITE: A Scalable Privacy-Preserving and Verifiable Collaborative Learning for Industrial IoT

TL;DR

SPRITE is a scalable, privacy-preserving, and verifiable collaborative learning framework designed for Industrial IoT, reducing resource overhead and ensuring trustworthy model aggregation even with untrusted cloud servers.

Contribution

SPRITE introduces a novel combination of secret sharing and homomorphic encryption to enhance privacy, verifiability, and scalability in IIoT collaborative learning.

Findings

Achieves 65% and 55% performance improvements over competitors for linear and logistic regression.

Reduces IIoT device communication overhead by 90%.

Proven secure in an honest-but-curious setting.

Abstract

Recently collaborative learning is widely applied to model sensitive data generated in Industrial IoT (IIoT). It enables a large number of devices to collectively train a global model by collaborating with a server while keeping the datasets on their respective premises. However, existing approaches are limited by high overheads and may also suffer from falsified aggregated results returned by a malicious server. Hence, we propose a Scalable, Privacy-preserving and veRIfiable collaboraTive lEarning (SPRITE) algorithm to train linear and logistic regression models for IIoT. We aim to reduce burden from resource-constrained IIoT devices and trust dependence on cloud by introducing fog as a middleware. SPRITE employs threshold secret sharing to guarantee privacy-preservation and robustness to IIoT device dropout whereas verifiable additive homomorphic secret sharing to ensure verifiability during model aggregation. We prove the security of SPRITE in an honest-but-curious setting where the cloud is untrustworthy. We validate SPRITE to be scalable and lightweight through theoretical overhead analysis and extensive testbed experimentation on an IIoT use-case with two real-world industrial datasets. For a large-scale industrial setup, SPRITE records 65% and 55% improved performance over its competitor for linear and logistic regressions respectively while reducing communication overhead for an IIoT device by 90%.