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Software Camouflage

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Part of the book series:Lecture Notes in Computer Science ((LNSC,volume 8352))

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Abstract

Obfuscation is a software technique aimed at protecting high-value programs against reverse-engineering. In embedded devices, it is harder for an attacker to gain access to the program machine code; of course, the program can still be very valuable, as for instance when it consists in a secret algorithm. In this paper, we investigate how obscurity techniques can be used to protect a secret customization of substitution boxes in symmetric ciphers, when the sole information available by the attacker is a side-channel. The approach relies on a combination of a universal evaluation algorithm for vectorial Boolean functions with indistinguishable opcodes that are randomly shuffled. The promoted solution is based on the noting that different logic opcodes, such as AND/OR or AND/XOR, happen to be very close one from each other from a side-channel leakage point of view. Moreover, our solution is very amenable to masking owing to the fact the substitution boxes are computed (combinationally).

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Notes

  1. 1.

    This is colloquially known ashardware “camo”; there are many such examples of technologies, such as this patent [12] by IBM and the tens of patents cited by this patent.

  2. 2.

    We notice that the paper [13] also tackles a similar issue, but requires to process simultaneously adecoil value, like indual-rail with precharge logics [14]. In oursoftware camouflage technique, the opcodes are balanced nativelyper se, without anydeus ex machina support.

  3. 3.

    MODELSIM is a commercial tool, sold by Mentor Graphics, capable of simulating a behavioral event-based HDL codes (e.g. VHDL or Verilog codes).

  4. 4.

    Notice that the storage of the sbox result isone option when computed in ANF, whereas it isinherent (i.e. unavoidable) to the computation with a Look-up-Table.

  5. 5.

    The work by Kimet al. [24] has shown that for some specific problems,e.g. when the sbox has a given structure (which is the case of the AES), minor improvements can be got by computing on half-words,e.g. on nibble instead of bytes. But this result does not negate the noting by Rivain and Prouff that computing masking schemes on larger bitwidths is faster than computing at the bit level.

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Acknowledgments

Parts of this work have been funded by theMARSHAL+ (Mechanisms Against Reverse-engineering for Secure Hardware and Algorithms) FUI #12 project, co-labellized by competitivity clusters System@tic and SCS.

We also thank the audience fromPHISIC ’13 for a positive feedback on this research.

Author information

Authors and Affiliations

  1. TELECOM-ParisTech, COMELEC dpt — UMR CNRS 5141, 39 rue Dareau, 75014, Paris, France

    Sylvain Guilley & Zakaria Najm

  2. Secure-IC S.A.S., 80 avenue des Buttes de Coësmes, 35700, Rennes, France

    Sylvain Guilley & Youssef Souissi

  3. XLIM — UMR CNRS 7252, 123, avenue Albert Thomas, 87060, Limoges Cedex, France

    Damien Marion & Antoine Wurcker

Authors
  1. Sylvain Guilley

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  2. Damien Marion

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  3. Zakaria Najm

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  4. Youssef Souissi

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  5. Antoine Wurcker

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Corresponding author

Correspondence toSylvain Guilley.

Editor information

Editors and Affiliations

  1. Dept. Commun. & Electronique, Télécom ParisTech, Paris, France

    Jean Luc Danger

  2. Concordia Institute for Information Syst Concordia University Research Chair Tier, Concordia University, Montreal, Québec, Canada

    Mourad Debbabi

  3. École des Mines, Nancy, France

    Jean-Yves Marion

  4. RST Department, Rm A105-2, Télécom SudParis, Evry, France

    Joaquin Garcia-Alfaro

  5. Dalhousie University, Halifax, Nova Scotia, Canada

    Nur Zincir Heywood

Algorithms Source Code

Algorithms Source Code

It is shown by Carlet in [22, page 11] that there exists a simple divide-and-conquer butterfly algorithm to compute the ANF from the truth-table (or vice-versa). It is called the “Fast Möbius Transform”. An implementation inpython is given in code listing 1.1, for\(n\rightarrow 1\) Boolean functions. As already underlined in Sect. 3.2, the very same code also works for\(n\rightarrow n\) vectorial Boolean functions.

figure a

The application of the code listing 1.1 to\(f=\mathtt{{SubBytes}}\) (array notedS_TT) is given asS_AND in the code listing 1.2. The values in the arrayS_TT are\(\{f(y), y\in \mathbb {F}_2^8\}\), in this order, whereas the values in the arrayS_ANF are\(\{a_y, y\in \mathbb {F}_2^8\}\) (recall Eq. (4)). In the same code listing, the functionanti_scare_eval appliesSubBytes on a byte\(x\), with the formula of Eq. (4). Furthermore, in this code, the\(y\)’s are shuffled (See Sect. 3.3) by a simple XOR with a random byte\(r\).

figure b

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Guilley, S., Marion, D., Najm, Z., Souissi, Y., Wurcker, A. (2014). Software Camouflage. In: Danger, J., Debbabi, M., Marion, JY., Garcia-Alfaro, J., Zincir Heywood, N. (eds) Foundations and Practice of Security. FPS 2013. Lecture Notes in Computer Science(), vol 8352. Springer, Cham. https://doi.org/10.1007/978-3-319-05302-8_8

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