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February  2018, 12(1): 115-122. doi: 10.3934/amc.2018007

Generalized nonlinearity of $ S$-boxes

Sugata Gangopadhyay 1, , Goutam Paul 2,, , Nishant Sinha 1, and  Pantelimon Stǎnicǎ 3,

1. 

Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
2. 

Cryptology and Security Research Unit, R. C. Bose Centre for Cryptology and Security, Indian Statistical Institute, Kolkata 700108, India
3. 

Department of Applied Mathematics, Naval Postgraduate School, Monterey, CA 93943-5216, USA

* Corresponding author: Goutam Paul

Nishant Sinha thanks IIT Roorkee for supporting his research

Received  September 2016 Published  March 2018

While analyzing $ S$-boxes, or vectorial Boolean functions, it is of interest to approximate its component functions by affine functions. In the usual attack models, it is assumed that all input vectors to an $ S$-box are equiprobable. The nonlinearity of an $ S$-box is defined, subject to this assumption. In this paper, we explore the possibility of linear cryptanalysis of an $ S$-box by introducing biased inputs and thus propose a generalized notion of nonlinearity along with a generalization of the Walsh-Hadamard spectrum of an $ S$-box.

Keywords: Nonlinearity, S-box, vectorial Boolean function, Walsh-Hadamard transform.
Mathematics Subject Classification: Primary: 06E30, 11T71; Secondary: 94A60.
Citation: Sugata Gangopadhyay, Goutam Paul, Nishant Sinha, Pantelimon Stǎnicǎ. Generalized nonlinearity of $ S$-boxes. Advances in Mathematics of Communications, 2018, 12 (1) : 115-122. doi: 10.3934/amc.2018007
References:
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P. Erdös and A. Rényi, On the evolution of random graphs, Publ. Math. Inst. Hungar. Acad. Sci., 5 (1960), 17-61.   Google Scholar

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E. Friedgut and Gil Kalai, Every monotone graph property has a sharp threshold, Proc. AMS, 124 (1996), 2293-3002.   Google Scholar

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S. Gangopadhyay, A. Kar Gangopadhyay, S. Pollatos and P. Stǎnicǎ, Cryptographic Boolean functions with biased inputs, Crypt. Commun. Discrete Struct. Seq., 9 (2017), 301-314.   Google Scholar

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Y. Lu and Y. Desmedt, Bias analysis of a certain problem with applications to E0 and Shannon ciper, in ICISC 2010, 2011, 16-28.  Google Scholar

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M. Matsui, Linear cryptanalysis method for DES cipher, in EUROCRYPT'93, Springer, 1994,386-397. Google Scholar

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R. O'Donnell, Analysis of Boolean Functions, Cambridge Univ. Press, 2014.  Google Scholar

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M. G. Parker, Generalised S-box nonlinearity, NESSIE Public Document, 11. 02. 03: NES/DOC/UIB/WP5/020/A. Google Scholar

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show all references

References:
[1]

P. Erdös and A. Rényi, On the evolution of random graphs, Publ. Math. Inst. Hungar. Acad. Sci., 5 (1960), 17-61.   Google Scholar

[2]

E. Friedgut and Gil Kalai, Every monotone graph property has a sharp threshold, Proc. AMS, 124 (1996), 2293-3002.   Google Scholar

[3]

S. Gangopadhyay, A. Kar Gangopadhyay, S. Pollatos and P. Stǎnicǎ, Cryptographic Boolean functions with biased inputs, Crypt. Commun. Discrete Struct. Seq., 9 (2017), 301-314.   Google Scholar

[4]

Y. Lu and Y. Desmedt, Bias analysis of a certain problem with applications to E0 and Shannon ciper, in ICISC 2010, 2011, 16-28.  Google Scholar

[5]

M. Matsui, Linear cryptanalysis method for DES cipher, in EUROCRYPT'93, Springer, 1994,386-397. Google Scholar

[6]

R. O'Donnell, Analysis of Boolean Functions, Cambridge Univ. Press, 2014.  Google Scholar

[7]

M. G. Parker, Generalised S-box nonlinearity, NESSIE Public Document, 11. 02. 03: NES/DOC/UIB/WP5/020/A. Google Scholar

[8]

D. R. Stinson, Cryptography: Theory and Practice, 3rd Edition, Chapman and Hall/CRC, 2005.  Google Scholar

Table 1.  Maximum bias without and with biased inputs for all DES S-boxes.
$F$ $S_1$ $S_2$ $S_3$ $S_4$ $S_5$ $S_6$ $S_7$ $S_8$
$\displaystyle \max_{{\bf{u}} \in {\mathbb{F}}_2^n}\epsilon({\bf{u}}, {\bf{v}} \cdot F)$ 0.219 0.219 0.219 0.156 0.219 0.188 0.281 0.188
$\displaystyle \max_{{\bf{u}} \in {\mathbb{F}}_2^n} \epsilon^{(p)}_{{\mathcal{S}}}({\bf{u}}, {\bf{v}}\cdot F)$ 0.494 0.494 0.497 0.489 0.494 0.491 0.494 0.494
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$F$ $S_1$ $S_2$ $S_3$ $S_4$ $S_5$ $S_6$ $S_7$ $S_8$
$\displaystyle \max_{{\bf{u}} \in {\mathbb{F}}_2^n}\epsilon({\bf{u}}, {\bf{v}} \cdot F)$ 0.219 0.219 0.219 0.156 0.219 0.188 0.281 0.188
$\displaystyle \max_{{\bf{u}} \in {\mathbb{F}}_2^n} \epsilon^{(p)}_{{\mathcal{S}}}({\bf{u}}, {\bf{v}}\cdot F)$ 0.494 0.494 0.497 0.489 0.494 0.491 0.494 0.494
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