Side-channel robustness analysis of masked assembly codes using a symbolic approach View Full Text


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Article Info

DATE

2019-03-16

AUTHORS

Inès Ben El Ouahma, Quentin L. Meunier, Karine Heydemann, Emmanuelle Encrenaz

ABSTRACT

Masking is a popular countermeasure against side-channel attacks, which randomizes secret data with random and uniform variables called masks. At software level, masking is usually added in the source code and its effectiveness needs to be verified. In this paper, we propose a symbolic method to verify side-channel robustness of masked programs. The analysis is performed at the assembly level since compilation and optimizations may alter the added protections. Our proposed method aims to verify that intermediate computations are statistically independent from secret variables using defined distribution inference rules. We verify the first round of a masked AES in 22 s and show that some secure algorithms or source codes are not leakage-free in their assembly implementations. More... »

PAGES

1-12

References to SciGraph publications

  • 2001-07-13. Timing Attacks on Implementations of Diffie-Hellman, RSA, DSS, and Other Systems in ADVANCES IN CRYPTOLOGY — CRYPTO ’96
  • 2016. Detecting Flawed Masking Schemes with Leakage Detection Tests in FAST SOFTWARE ENCRYPTION
  • 2014. Higher Order Masking of Look-Up Tables in ADVANCES IN CRYPTOLOGY – EUROCRYPT 2014
  • 2017. Parallel Implementations of Masking Schemes and the Bounded Moment Leakage Model in ADVANCES IN CRYPTOLOGY – EUROCRYPT 2017
  • 2017-07-29. Mind the Gap: Towards Secure 1st-Order Masking in Software in CONSTRUCTIVE SIDE-CHANNEL ANALYSIS AND SECURE DESIGN
  • 2015. On the Cost of Lazy Engineering for Masked Software Implementations in SMART CARD RESEARCH AND ADVANCED APPLICATIONS
  • 2006. An AES Smart Card Implementation Resistant to Power Analysis Attacks in ROBOCUP 2005: ROBOT SOCCER WORLD CUP IX
  • 2001-09-20. A Sound Method for Switching between Boolean and Arithmetic Masking in CRYPTOGRAPHIC HARDWARE AND EMBEDDED SYSTEMS — CHES 2001
  • 2014. Synthesis of Masking Countermeasures against Side Channel Attacks in COMPUTER AIDED VERIFICATION
  • 2015-08-12. Conversion from Arithmetic to Boolean Masking with Logarithmic Complexity in FAST SOFTWARE ENCRYPTION
  • 2015-04-14. Verified Proofs of Higher-Order Masking in ADVANCES IN CRYPTOLOGY -- EUROCRYPT 2015
  • 2017-08-25. Fast Leakage Assessment in CRYPTOGRAPHIC HARDWARE AND EMBEDDED SYSTEMS – CHES 2017
  • 2007. Side Channel Cryptanalysis of a Higher Order Masking Scheme in CRYPTOGRAPHIC HARDWARE AND EMBEDDED SYSTEMS - CHES 2007
  • 2004. Provably Secure Masking of AES in SELECTED AREAS IN CRYPTOGRAPHY
  • 2014. Higher-Order Threshold Implementations in ADVANCES IN CRYPTOLOGY – ASIACRYPT 2014
  • 2013. Masking against Side-Channel Attacks: A Formal Security Proof in ADVANCES IN CRYPTOLOGY – EUROCRYPT 2013
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s13389-019-00205-7

    DOI

    http://dx.doi.org/10.1007/s13389-019-00205-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112829978


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