A White-Box Speck Implementation Using Self-equivalence Encodings View Full Text


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Chapter Info

DATE

2022-06-18

AUTHORS

Joachim Vandersmissen , Adrián Ranea , Bart Preneel

ABSTRACT

In 2002, Chow et al. initiated the formal study of white-box cryptography and introduced the CEJO framework. Since then, various white-box designs based on their framework have been proposed, all of them broken. Ranea and Preneel proposed a different method in 2020, called self-equivalence encodings and analyzed its security for AES. In this paper, we apply this method to generate the first academic white-box speck implementations using self-equivalence encodings. Although we focus on speck in this work, our design could easily be adapted to protect other add-rotate-xor (ARX) ciphers. Then, we analyze the security of our implementation against key-recovery attacks. We propose an algebraic attack to fully recover the master key and external encodings from a white-box speck implementation, with limited effort required. While this result shows that the linear and affine self-equivalences of speck are insecure, we hope that this negative result will spur additional research in higher-degree self-equivalence encodings for white-box cryptography. Finally, we created an open-source Python project implementing our design, publicly available at https://github.com/jvdsn/white-box-speck. We give an overview of five strategies to generate output code, which can be used to improve the performance of the white-box implementation. We compare these strategies and determine how to generate the most performant white-box speck code. Furthermore, this project could be employed to test and compare the efficiency of attacks on white-box implementations using self-equivalence encodings. More... »

PAGES

771-791

Book

TITLE

Applied Cryptography and Network Security

ISBN

978-3-031-09233-6
978-3-031-09234-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-031-09234-3_38

DOI

http://dx.doi.org/10.1007/978-3-031-09234-3_38

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1148760562


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