With the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as object detection using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge servers. However, data privacy becomes a major concern for vehicular edge computing, as sensitive sensor data from vehicles can be observed and used by edge servers. We aim to address the privacy problem by protecting both vehicles’ sensor data and the detection results. In this paper, we present vehicle–edge cooperative deep-learning networks with privacy protection for object-detection tasks, named vePOD for short. In vePOD, we leverage the additive secret sharing theory to develop secure functions for every layer in an object-detection convolutional neural network (CNN). A vehicle’s sensor data is split and encrypted into multiple secret shares, each of which is processed on an edge server by going through the secure layers of a detection network. The detection results can only be obtained by combining the partial results from the participating edge servers. We have developed proof-of-concept detection networks with secure layers: vePOD Faster R-CNN (two-stage detection) and vePOD YOLO (single-stage detection). Experimental results on public datasets show that vePOD does not degrade the accuracy of object detection and, most importantly, it protects data privacy for vehicles. The execution of a vePOD object-detection network with secure layers is orders of magnitude faster than the existing approaches for data privacy. To the best of our knowledge, this is the first work that targets privacy protection in object-detection tasks with vehicle–edge cooperative computing.
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Securing Tire Pressure Monitoring System for Vehicular Privacy
Modern vehicles are equipped with vehicular sensors for smart navigation, vehicle state awareness, and other intelligent operations. Despite the previous belief that the sensor operations stay within a vehicle, as it is designed to be, we study information leakage through the tire pressure monitoring system (TPMS) sensors and the corresponding privacy breach. We demonstrate that, using a low-cost and off-the-shelf software defined radio (SDR), an unauthorized attacker can track uniquely-identifiable sensor IDs up to 40 meters away from the vehicle. To address the issue and protect vehicular privacy, we also propose an effective and lightweight TPMS ID randomization scheme and analyze its security and the implementation costs.
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- Award ID(s):
- 1922410
- NSF-PAR ID:
- 10324016
- Date Published:
- Journal Name:
- IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CCNC49032.2021.9369576
