TL;DR: It is proved that this cipher is indistinguishable from a random permutation of $\{0, 1\}^{2n}$, for any adversary who has oracle access to the public permutations and to an encryption/decryption oracle, as long as the number of queries is $o (2^{n/2})$.
Abstract: The r-rounds Even-Mansour block cipher uses r public permutations of {0, 1} and r+1 secret keys. An attack on this construction was described in [6], for r = 2, 3. Although this attack is only marginally better than brute force, it is based on an interesting observation (due to [10]): for a ”typical” permutation P , the distribution of P (x)⊕ x is not uniform. To address this, and other potential threats that might stem from this observation in this (or other) context, we introduce the notion of a “balanced permutation” for which the distribution of P (x) ⊕ x is uniform, and show how to generate families of balanced permutations from the Feistel construction. This allows us to define a 2n-bit block cipher from the 2-rounds Even-Mansour scheme. The cipher uses public balanced permutations of {0, 1}, which are based on two public permutations of {0, 1}. By construction, this cipher is immune against attacks that rely on the non-uniform behavior of P (x)⊕x. We prove that this cipher is indistinguishable from a random permutation of {0, 1}, for any adversary who has oracle access to the public permutations and to an encryption/decryption oracle, as long as the number of queries is o(2). As a practical example, we discuss the properties and the performance of a 256-bit block cipher that is based on AES.
TL;DR: The notion ofbalanced permutations is introduced and the capacity of balanced permutation codes is derived and simple interleaving methods for permutation code constructions are described and shown to approach capacity.
Abstract: Motivated by charge balancing constraints for rank modulation schemes, we introduce the notion of balanced permutations and derive the capacity of balanced permutation codes. We also describe simple interleaving methods for permutation code constructions and show that they approach capacity
TL;DR: Balanced permutation graph as mentioned in this paper is a graph with the same vertex set as G π, but with adjacency condition ij ϵ E if and only if i + j = π(i) + π (j).
Abstract: Given any permutation π of the set {1, 2, 3, …, n}, the permutation graph G π is classical. (If V = {1, 2, 3,…n}, then G π = (V, E) with V as vertex set and π(i) π(j) ϵ E if and only if i π(j)). We define another graph called balanced permutation graph, with the same vertex set {1, 2, 3,…, n} as G π , but with adjacency condition ij ϵ E if and only if i + j = π(i) + π(j). Some properties of balanced permutation graph are discussed in this paper.
TL;DR: In this article, the capacity of balanced permutation codes is derived and simple interleaving methods for permutation code constructions are described. But they do not address the problem of charge balancing constraints for rank modulation.
Abstract: Motivated by charge balancing constraints for rank modulation schemes, we introduce the notion of balanced permutations and derive the capacity of balanced permutation codes. We also describe simple interleaving methods for permutation code constructions and show that they approach capacity.
TL;DR: It turns out that balanced permutation reference distributions do not have the correct null behavior, which can be traced to their lack of a group structure, and they can give p-values that are too permissive to varying degrees.
Abstract: This paper takes a close look at balanced permutations, a recently developed sample reuse method with applications in bioinformatics. It turns out that balanced permutation reference distributions do not have the correct null behavior, which can be traced to their lack of a group structure. We find that they can give p-values that are too permissive to varying degrees. In particular the observed test statistic can be larger than that of all B balanced permutations of a data set with a probability much higher than 1/(B + 1), even under the null hypothesis.