Privacy-Preserving IoT in Connected Aircraft Cabin

TL;DR

This paper introduces a framework integrating Privacy-Enhancing Technologies with secure aircraft IoT communication protocols, analyzing their impact on privacy, utility, and performance in resource-constrained environments.

Contribution

It proposes a configurable PET layer atop existing aircraft IoT protocols and empirically evaluates differential privacy and secret sharing schemes in realistic settings.

Findings

PETs' computational overhead is often negligible compared to network latencies.

Architectural choices significantly impact latency and performance.

The framework enables trustworthy data sharing in aircraft IoT systems.

Abstract

The proliferation of IoT devices in shared, multi-vendor environments like the modern aircraft cabin creates a fundamental conflict between the promise of data collaboration and the risks to passenger privacy, vendor intellectual property (IP), and regulatory compliance. While emerging standards like the Cabin Secure Media-Independent Messaging (CSMIM) protocol provide a secure communication backbone, they do not resolve data governance challenges at the application layer, leaving a privacy gap that impedes trust. This paper proposes and evaluates a framework that closes this gap by integrating a configurable layer of Privacy-Enhancing Technologies (PETs) atop a CSMIM-like architecture. We conduct a rigorous, empirical analysis of two pragmatic PETs: Differential Privacy (DP) for statistical sharing, and an additive secret sharing scheme (ASS) for data obfuscation. Using a high-fidelity testbed with resource-constrained hardware, we quantify the trade-offs between data privacy, utility, and computing performance. Our results demonstrate that the computational overhead of PETs is often negligible compared to inherent network and protocol latencies. We prove that architectural choices, such as on-device versus virtualized processing, have a far greater impact on end-to-end latency and computational performance than the PETs themselves. The findings provide a practical roadmap for system architects to select and configure appropriate PETs, enabling the design of trustworthy collaborative IoT ecosystems in avionics and other critical domains.