User-Defined Privacy Preserving Data Sharing for Connected Autonomous Vehicles Utilizing Edge Computing
User-Defined Privacy Preserving Data Sharing for Connected Autonomous Vehicles Utilizing Edge Computing
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DOI：
10.1145/3583740.3628436
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Tianyu Bai;Qing Yang;Song Fu
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文献类型：
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作者：
Tianyu Bai;Qing Yang;Song Fu
本文提出了一种适用于互联自动驾驶车辆(CAV)的隐私保护数据共享框架。Precision允许用户在与其他车辆共享数据之前定义他们希望保护隐私的对象或部件。它利用安全分割和修补技术来保护车辆的敏感数据。Precision探索各种优势，以减轻资源密集型深度学习工作负载。为了保证边缘处理过程中的数据保密性，Precision利用加性秘密共享理论定义了深度神经网络的安全函数。介绍了两种安全的DNN模型：安全SegNet和安全上下文编码器，并详细说明了如何开发安全的CNN层以及在构建这些层时使用的安全函数。我们已经实现了一个精确的原型，并对其性能进行了评估。实验结果表明，该算法是轻量级的，在3.47秒内实现了安全分割，在0.99秒内实现了安全修复。PRECISTION的推理输出与原始DNN的推理输出保持一致，同时保护了数据隐私。据我们所知，Precision是同类产品中第一个为骑士队之间的传感器数据共享提供用户定义的隐私保护的公司。
In this paper, we present PRECISE, a novel privacy preserving data sharing framework for connected autonomous vehicles (CAVs). PRECISE allows users to define the objects or parts that they wish to protect privacy before sharing data with other vehicles. It leverages secure segmentation and inpainting technologies to protect sensitive data of vehicles. PRECISE explores the edges to offload resource-intensive deep learning workloads. To ensure data privacy in the processing on edge, PRECISE leverages additive secret sharing theory to define secure functions for deep neural networks (DNNs). Two secure DNN models, Secure SegNet and Secure Context Encoder, are introduced, along with detailed explanations of how to develop secure CNN layers and the secure functions used in building these layers. We have implemented a prototype of PRECISE and evaluated its performance. The experimental results demon-strate that PRECISE is lightweight, achieving secure segmentation in 3.47 seconds and secure inpainting in 0.99 seconds. The inference outputs from PRECISE remain the same as those from the original DNNs, while data privacy is protected. To the best of our knowledge, PRECISE is the first of its kind to provide user-defined privacy protection for sensor data sharing among CAVs.