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Physically Unclonable Function-Based Security and Privacy in RFID Systems

Bolotnyy, Leonid, Robins, Gabriel·2007·Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom'07)

Abstract

Radio frequency identification (RFID) is an increasingly popular technology that uses radio signals for object identification. Tracking and authentication in RFID tags have raised many privacy and security concerns. On the other hand, known privacy and security cryptographic defenses are too hardware-expensive to incorporate into low-cost RFID tags. In this paper, we propose hardware-based approaches to RFID security that rely on physically unclonable functions (PUFs). These functions exploit the inherent variability of wire delays and parasitic gate delays in manufactured circuits, and may be implemented with an order-of-magnitude reduction in gate count as compared with traditional cryptographic functions. We describe protocols for privacy-preserving tag identification and secure message authentication codes. We compare PUFs to digital cryptographic functions, address other uses of PUFs to enhance RFID security and suggest interesting directions for future research. The proposed solutions are efficient, practical, and appropriate for low-cost RFID systems

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Metadata

Authors
Bolotnyy, L., & Robins, G.
Venue
Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom'07)
Publisher
IEEE
Published
2007
Pages
211-220
ISSN
2474-249X (ISSN-L)
OpenAlex
W2122394567
Cited by
196
References
0
FWCI
10.46
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Physically Unclonable Function-Based Security and Privacy in RFID Systems. https://app.cochat.ai/papers/10.1109/percom.2007.26
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Bolotnyy, L., & Robins, G. (2007). Physically Unclonable Function-Based Security and Privacy in RFID Systems. Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom'07). https://doi.org/10.1109/percom.2007.26

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.