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.

翻译：在飞机客舱等共享、多供应商的现代环境中，物联网设备的激增引发了数据协作前景与乘客隐私风险、供应商知识产权保护及法规遵从之间的根本性矛盾。尽管新兴标准如客舱安全媒体无关消息协议为安全通信提供了骨干支撑，但未能解决应用层的数据治理挑战，导致隐私缺口阻碍信任建立。本文提出并评估一种框架，通过在类CSMIM架构上集成可配置的隐私增强技术层来弥合这一缺口。我们对两种实用型PET进行了严谨的实证分析：用于统计共享的差分隐私，以及用于数据混淆的加法秘密共享方案。通过采用资源受限硬件构建的高保真测试平台，我们量化了数据隐私性、实用性与计算性能之间的权衡关系。实验结果表明，与固有的网络及协议延迟相比，PET的计算开销通常可忽略不计。我们证明架构选择对端到端延迟和计算性能的影响远大于PET本身，例如设备端处理与虚拟化处理的差异。研究结果为系统架构师提供了选择与配置合适PET的实践路径，从而支持在航空电子及其他关键领域构建可信的协作式物联网生态系统。