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Non-Linear Collision Analysis

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Radio Frequency Identification: Security and Privacy Issues (RFIDSec 2015)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 8651))

Abstract

As an unsolved issue for embedded crypto solutions, side channel attacks are challenging the security of the Internet of things. Due to the advancement of chip technology, the nature of side channel leakage becomes hard to characterize with a fixed leakage model. In this work, a new non-linear collision attack is proposed in the pursuit of the side channel distinguishers with minimal assumption of leakage behavior. The attack relies on a weaker assumption than classical DPA: it does not require a specific leakage model. The mechanism of collision generation enables independent recovery of partial keys so that for the first time the collision attack can be fairly compared with other standard side channel distinguishers. The efficiency of this attack has been verified by experiments on an unprotected microcontroller implementation of AES. Its immunity to modeling errors is confirmed through simulation of a broad range of leakage functions.

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Notes

  1. 1.

    It can easily be translated to last round SubBytes with known ciphertexts.

  2. 2.

    As pointed out in [19], the near generic 7LSB power model for AES does not perform well for the MIA and it even fails catastrophically in strong signal setting.

  3. 3.

    It can also be considered that \(Y_L\) is mapped non-linearly to \(U\) before generating leakages. This is similar to the challenging scenario discussed in Sect. 4.2.

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Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. #1261399 and Grant No. #1314770. We would like to thank the anonymous reviewers for their helpful comments.

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Ye, X., Chen, C., Eisenbarth, T. (2014). Non-Linear Collision Analysis. In: Saxena, N., Sadeghi, AR. (eds) Radio Frequency Identification: Security and Privacy Issues. RFIDSec 2015. Lecture Notes in Computer Science(), vol 8651. Springer, Cham. https://doi.org/10.1007/978-3-319-13066-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-13066-8_13

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