Message Time of Arrival Codes: A Fundamental Primitive for Secure Distance Measurement
November 25, 2019 ยท Declared Dead ยท ๐ IEEE Symposium on Security and Privacy
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Patrick Leu, Mridula Singh, Marc Roeschlin, Kenneth G. Paterson, Srdjan Capkun
arXiv ID
1911.11052
Category
cs.CR: Cryptography & Security
Citations
30
Venue
IEEE Symposium on Security and Privacy
Last Checked
3 months ago
Abstract
Secure distance measurement and therefore secure Time-of-Arrival (ToA) measurement is critical for applications such as contactless payments, passive-keyless entry and start systems, and navigation systems. This paper initiates the study of Message Time of Arrival Codes (MTACs) and their security. MTACs represent a core primitive in the construction of systems for secure ToA measurement. By surfacing MTACs in this way, we are able for the first time to formally define the security requirements of physical-layer measures that protect ToA measurement systems against attacks. Our viewpoint also enables us to provide a unified presentation of existing MTACs (such as those proposed in distance-bounding protocols and in a secure distance measurement standard) and to propose basic principles for protecting ToA measurement systems against attacks that remain unaddressed by existing mechanisms. We also use our perspective to systematically explore the tradeoffs between security and performance that apply to all signal modulation techniques enabling ToA measurements.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Cryptography & Security
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Membership Inference Attacks against Machine Learning Models
R.I.P.
๐ป
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
๐ป
Ghosted
Practical Black-Box Attacks against Machine Learning
R.I.P.
๐ป
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
๐ป
Ghosted
Extracting Training Data from Large Language Models
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted