A Privacy-Preserving Protocol Level Approach to Prevent Machine Learning Modelling Attacks on PUFs in the Presence of Semi-Honest Verifiers-Reference-Cited by-同舟云学术

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A Privacy-Preserving Protocol Level Approach to Prevent Machine Learning Modelling Attacks on PUFs in the Presence of Semi-Honest Verifiers

Published:2023-05-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)
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Author:

Millwood Owen¹,Hongming Fei²,Gope Prosanta¹,Narlı Oğuz³,Pehlivanoğlu Meltem Kurt³,Kavun Elif Bilge⁴,Sikdar Biplab²

Affiliation:

1. The University of Sheffield,Department of Computer Science,Sheffield,UK

2. The National University of Singapore,Department of Electrical and Computer Engineering,Singapore

3. Kocaeli University,Department of Computer Engineering,Kocaeli,Turkey

4. University of Passau,Secure Intelligent Systems Research Group,Passau,Germany

Funder

Royal Society

National Research Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10132842/10132914/10133804.pdf?arnumber=10133804

Reference29 articles.

1. RPUF: Physical Unclonable Function with Randomized Challenge to resist modeling attack

2. Towards fast and accurate machine learning attacks of feed-forward arbiter PUFs

3. Lightweight secure PUFs

4. Modeling attacks on physical unclonable functions

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