The kth-order nonhomomorphicity of S-boxes

Nonhomomorphicity is a new nonlinearity criterion of a mapping or S-box used in a private key encryption algorithm. An important advantage of nonhomomorphicity over other nonlinearity criteria is that the value of nonhomomorphicity is easy to estimate by the use of a fast statistical method. Due to the Law of Large Numbers, such a statistical method is highly reliable. Major contributions of this paper are (1) to explicitly express the nonhomomorphicity by other nonlinear characteristics, (2) to identify tight upper and lower bounds on nonhomomorphicity, and (3) to find the mean of nonhomomorphicity over all the S-boxes with the same size. It is hoped that these results on nonhomomorphicity facilitate the analysis and design of S-boxes

Systematically Quantifying Cryptanalytic Non-Linearities in Strong PUFs

Physically Unclonable Functions~(PUFs) with large challenge space~(also called Strong PUFs) are promoted for usage in authentications and various other cryptographic and security applications. In order to qualify for these cryptographic applications, the Boolean functions realized by PUFs need to possess a high non-linearity~(NL). However, with a large challenge space~(usually $\geq 64$ bits), measuring NL by classical techniques like Walsh transformation is computationally infeasible. In this paper, we propose the usage of a heuristic-based measure called non-homomorphicity test which estimates the NL of Boolean functions with high accuracy in spite of not needing access to the entire challenge-response set. We also combine our analysis with a technique used in linear cryptanalysis, called Piling-up lemma, to measure the NL of popular PUF compositions. As a demonstration to justify the soundness of the metric, we perform extensive experimentation by first estimating the NL of constituent Arbiter/Bistable Ring PUFs using the non-homomorphicity test, and then applying them to quantify the same for their XOR compositions namely XOR Arbiter PUFs and XOR Bistable Ring PUF. Our findings show that the metric explains the impact of various parameter choices of these PUF compositions on the NL obtained and thus promises to be used as an important objective criterion for future efforts to evaluate PUF designs. While the framework is not representative of the machine learning robustness of PUFs, it can be a useful complementary tool to analyze the cryptanalytic strengths of PUF primitives

Ongoing Research Areas in Symmetric Cryptography

This report is a deliverable for the ECRYPT European network of excellence in cryptology. It gives a brief summary of some of the research trends in symmetric cryptography at the time of writing. The following aspects of symmetric cryptography are investigated in this report: • the status of work with regards to different types of symmetric algorithms, including block ciphers, stream ciphers, hash functions and MAC algorithms (Section 1); • the recently proposed algebraic attacks on symmetric primitives (Section 2); • the design criteria for symmetric ciphers (Section 3); • the provable properties of symmetric primitives (Section 4); • the major industrial needs in the area of symmetric cryptography (Section 5)

D.STVL.9 - Ongoing Research Areas in Symmetric Cryptography

This report gives a brief summary of some of the research trends in symmetric cryptography at the time of writing (2008). The following aspects of symmetric cryptography are investigated in this report: • the status of work with regards to different types of symmetric algorithms, including block ciphers, stream ciphers, hash functions and MAC algorithms (Section 1); • the algebraic attacks on symmetric primitives (Section 2); • the design criteria for symmetric ciphers (Section 3); • the provable properties of symmetric primitives (Section 4); • the major industrial needs in the area of symmetric cryptography (Section 5)

The Nonhomomorphicity of Boolean Functions

We introduce the notion of nonhomomorphicity as an alternative criterion that forecasts nonlinear characteristics of a Boolean function. Although both nonhomomorphicity and nonlinearity reflect a "difference" between a Boolean function and all the affine functions, they are measured from different perspectives. We are interested in nonhomomorphicity due to several reasons that include (1) unlike other criteria, we have not only established tight lower and upper bounds on the nonhomomorphicity of a function, but also precisely identified the mean of nonhomomorphicity over all the Boolean functions on the same vector space, (2) the nonhomomorphicity of a function can be estimated efficiently, and in fact, we demonstrate a fast statistical method that works both on large and small dimensional vector spaces

Improving bounds on probabilistic affine tests to estimate the nonlinearity of Boolean functions

In this paper we want to estimate the nonlinearity of Boolean functions, by probabilistic methods, when it is computationally very expensive, or perhaps not feasible to compute the full Walsh transform (which is the case for almost all functions in a larger number of variables, say more than 30). Firstly, we significantly improve upon the bounds of Zhang and Zheng (1999) on the probabilities of failure of affinity tests based on nonhomomorphicity, in particular, we prove a new lower bound that we have previously conjectured. This new lower bound generalizes the one of Bellare et al. (IEEE Trans. Inf. Theory 42(6), 1781–1795 1996) to nonhomomorphicity tests of arbitrary order. Secondly, we prove bounds on the probability of failure of a proposed affinity test that uses the BLR linearity test. All these bounds are expressed in terms of the function’s nonlinearity, and we exploit that to provide probabilistic methods for estimating the nonlinearity based upon these affinity tests. We analyze our estimates and conclude that they have reasonably good accuracy, particularly so when the nonlinearity is low

Improving bounds on probabilistic affine tests to estimate the nonlinearity of Boolean functions

17 USC 105 interim-entered record; under temporary embargo.In this paper we want to estimate the nonlinearity of Boolean functions, by probabilistic methods, when it is computationally very expensive, or perhaps not feasible to compute the full Walsh transform (which is the case for almost all functions in a larger number of variables, say more than 30). Firstly, we significantly improve upon the bounds of Zhang and Zheng (1999) on the probabilities of failure of affinity tests based on nonhomomorphicity, in particular, we prove a new lower bound that we have previously conjectured. This new lower bound generalizes the one of Bellare et al. (IEEE Trans. Inf. Theory 42(6), 1781– 1795 1996) to nonhomomorphicity tests of arbitrary order. Secondly, we prove bounds on the probability of failure of a proposed affinity test that uses the BLR linearity test. All these bounds are expressed in terms of the function’s nonlinearity, and we exploit that to provide probabilistic methods for estimating the nonlinearity based upon these affinity tests. We analyze our estimates and conclude that they have reasonably good accuracy, particularly so when the nonlinearity is low.U.S. Government affiliation is unstated in article text