Eingerprint: Robust Energy-related Fingerprinting for Passive RFID Tags


Xingyu Chen, Jia Liu, Xia Wang, Haisong Liu, Dong Jiang, and Lijun Chen, Nanjing University


RFID tag authentication is challenging because most advanced cryptographic algorithms cannot be afforded by passive tags. Recent physical-layer identification utilizes unique features of RF signals as the fingerprint to authenticate a tag. This approach is effective but difficult for practical use because it either requires a purpose-built device to extract the signal features or is sensitive to environmental conditions. In this paper, we present a new energy-related fingerprint called Eingerprint to authenticate passive tags with commodity RFID devices. The competitive advantage of Eingerprint is that it is fully compatible with the RFID standard EPCglobal Gen2, which makes it more applicable and scalable in practice. Besides, it takes the electrical energy stored in a tag's resistor-capacitor (RC) circuit as the fingerprint, which is robust to environmental changes such as tag position, communication distance, transmit power, and multi-path effects. We propose a new metric called persistence time to indirectly estimate the energy level in the RC circuit. A select-query based scheme is designed to extract the persistence time by flipping and observing a flag in the tag's volatile memory. We implement a prototype of Eingerprint with commodity RFID devices without any modifications to the hardware or the firmware. Experiment results show that Eingerprint is able to achieve a high authentication accuracy of 99.4% when three persistence times are used, regardless of device diversity and environmental conditions.

NSDI '20 Open Access Sponsored by NetApp

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@inproceedings {246298,
author = {Xingyu Chen and Jia Liu and Xia Wang and Haisong Liu and Dong Jiang and Lijun Chen},
title = {Eingerprint: Robust Energy-related Fingerprinting for Passive {RFID} Tags },
booktitle = {17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20)},
year = {2020},
isbn = {978-1-939133-13-7},
address = {Santa Clara, CA},
pages = {1101--1113},
url = {https://www.usenix.org/conference/nsdi20/presentation/chen},
publisher = {USENIX Association},
month = feb

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