A Bit-Vector Differential Model for the Modular Addition by a Constant

ARX algorithms are a class of symmetric-key algorithms constructed by Addition, Rotation, and XOR, which achieve the best software performances in low-end microcontrollers. To evaluate the resistance of an ARX cipher against differential cryptanalysis and its variants, the recent automated methods employ constraint satisfaction solvers, such as SMT solvers, to search for optimal characteristics. The main difficulty to formulate this search as a constraint satisfaction problem is obtaining the differential models of the non-linear operations, that is, the constraints describing the differential probability of each non-linear operation of the cipher. While an efficient bit-vector differential model was obtained for the modular addition with two variable inputs, no differential model for the modular addition by a constant has been proposed so far, preventing ARX ciphers including this operation from being evaluated with automated methods. In this paper, we present the first bit-vector differential model for the n-bit modular addition by a constant input. Our model contains O(log2(n)) basic bit-vector constraints and describes the binary logarithm of the differential probability. We also represent an SMT-based automated method to look for differential characteristics of ARX, including constant additions, and we provide an open-source tool ArxPy to find ARX differential characteristics in a fully automated way. To provide some examples, we have searched for related-key differential characteristics of TEA, XTEA, HIGHT, and LEA, obtaining better results than previous works. Our differential model and our automated tool allow cipher designers to select the best constant inputs for modular additions and cryptanalysts to evaluate the resistance of ARX ciphers against differential attacks.acceptedVersio

Automatic Search for the Best Trails in ARX: Application to Block Cipher Speck

We propose the first adaptation of Matsui's algorithm for finding the best differential and linear trails to the class of ARX ciphers. It is based on a branch-and-bound search strategy, does not use any heuristics and returns optimal results. The practical application of the new algorithm is demonstrated on reduced round variants of block ciphers from the Speck family. More specifically, we report the probabilities of the best differential trails for up to 10, 9, 8, 7, and 7 rounds of Speck32, Speck48, Speck64, Speck96 and Speck128 respectively, together with the exact number of differential trails that have the best probability. The new results are used to compute bounds, under the Markov assumption, on the security of Speck against single-trail differential cryptanalysis. Finally, we propose two new ARX primitives with provable bounds against single-trail differential and linear cryptanalysis -- a long standing open problem in the area of ARX design

Pioglitazone-mediated peroxisome proliferator-activated receptor γ activation aggravates murine immune-mediated hepatitis

The nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ) regulates target gene expression upon ligand binding. Apart from its effects on metabolism, PPARγ activity can inhibit the production of pro-inflammatory cytokines by several immune cells, including dendritic cells and macrophages. In chronic inflammatory disease models, PPARγ activation delays the onset and ameliorates disease severity. Here, we investigated the effect of PPARγ activation by the agonist Pioglitazone on the function of hepatic immune cells and its effect in a murine model of immune-mediated hepatitis. Cytokine production by both liver sinusoidal endothelial cells (IL-6) and in T cells ex vivo (IFNγ) was decreased in cells from Pioglitazone-treated mice. However, PPARγ activation did not decrease pro-inflammatory tumor necrosis factor alpha TNFα production by Kupffer cells after Toll-like receptor (TLR) stimulation ex vivo. Most interestingly, although PPARγ activation was shown to ameliorate chronic inflammatory diseases, it did not improve hepatic injury in a model of immune-mediated hepatitis. In contrast, Pioglitazone-induced PPARγ activation exacerbated D-galactosamine (GalN)/lipopolysaccharide (LPS) hepatitis associated with an increased production of TNFα by Kupffer cells and increased sensitivity of hepatocytes towards TNFα after in vivo Pioglitazone administration. These results unravel liver-specific effects of Pioglitazone that fail to attenuate liver inflammation but rather exacerbate liver injury in an experimental hepatitis model