In this work we present a duplex-based authenticated encryption scheme Friet based on a new permutation called Friet-P. We designed Friet-P with a novel approach for cryptographic permutations and block ciphers that takes fault-attack resistance into account and that we introduce in this paper. In this method, we build a permutation f_C to be embedded in a larger one f. First, we define f as a sequence of steps that all abide a chosen error-correcting code C, i.e., that map C-codewords to C-codewords. Then, we embed f_C in f by first encoding its input to an element of C, applying f and then decoding back from C. This last step detects a fault when the output of f is not in C. We motivate the design of the permutation we use in Friet and report on performance in soft- and hardware. We evaluate the fault-detection capabilities of the software and simulated hardware implementations with attacks. Finally, we perform a leakage evaluation. Our code is available at https://github.com/thisimon/Friet.git.

In CHES 2017, Jean et al. presented a paper on “Bit-Sliding” in which the authors proposed lightweight constructions for SPN based block ciphers like AES, PRESENT and SKINNY. The main idea behind these constructions was to reduce the length of the datapath to 1 bit and to reformulate the linear layer for these ciphers so that they require fewer scan flip-flops (which have built-in multiplexer functionality and so larger in area as compared to a simple flip-flop). In this paper, we develop their idea even further in few separate directions.First, we prove that given an arbitrary linear transformation, it is always possible to construct the linear layer using merely 2 scan flip-flops. This points to an optimistic venue to follow to gain further GE reductions, yet the straightforward application of the techniques in our proof to PRESENT and GIFT leads to inefficient implementations of the linear layer, as reducing ourselves to 2 scan flip-flops setting requires thousands of clock cycles and leads to very high latency.Equipped with the well-established formalism on permutation groups, we explore whether we can reduce the number of clock cycles to a practical level, i.e. few hundreds, by adding few more pairs of scan flip flops. For PRESENT, we show that 4 (resp. 8, 12) scan flip-flops are sufficient to complete the permutation layer in 384 (resp. 256, 128) clock cycles. For GIFT, we show that 4 (resp. 8, 10) scan flip flops correspond to 320 (resp. 192, 128) clock cycles. Finally, in order to provide the best of the two worlds (i.e. circuit area and latency), we push our scan flip-flop choices even further to completely eliminate the latency incurred by the permutation layer, without compromising our stringent GE budget. We show that not only 12 scan flip flops are sufficient to execute PRESENT permutation in 64 clock cycles, but also the same scan flip flops can be used readily in a combined encryption decryption circuit. Our final design of PRESENT and GIFT beat the record of Jean et al. and Banik et al. in both latency and in circuit-size metric. We believe that the techniques presented in our work can also be used at choosing bit-sliding-friendly linear layer permutations for the future SPN-based designs.

We concentrate on machine learning techniques used for profiled sidechannel analysis in the presence of imbalanced data. Such scenarios are realistic and often occurring, for instance in the Hamming weight or Hamming distance leakage models. In order to deal with the imbalanced data, we use various balancing techniques and we show that most of them help in mounting successful attacks when the data is highly imbalanced. Especially, the results with the SMOTE technique are encouraging, since we observe some scenarios where it reduces the number of necessary measurements more than 8 times. Next, we provide extensive results on comparison of machine learning and side-channel metrics, where we show that machine learning metrics (and especially accuracy as the most often used one) can be extremely deceptive. This finding opens a need to revisit the previous works and their results in order to properly assess the performance of machine learning in side-channel analysis.

Energy optimization is an important design aspect of lightweight cryptography. Since low energy ciphers drain less battery, they are invaluable components of devices that operate on a tight energy budget such as handheld devices or RFID tags. At Asiacrypt 2015, Banik et al. presented the block cipher family Midori which was designed to optimize the energy consumed per encryption and which reduces the energy consumption by more than 30% compared to previous block ciphers. However, if one has to encrypt/decrypt longer streams of data, i.e. for bulk data encryption/decryption, it is expected that a stream cipher should perform even better than block ciphers in terms of energy required to encrypt. In this paper, we address the question of designing low energy stream ciphers. To this end, we analyze for common stream cipher design components their impact on the energy consumption. Based on this, we give arguments why indeed stream ciphers allow for encrypting long data streams with less energy than block ciphers and validate our findings by implementations. Afterwards, we use the analysis results to identify energy minimizing design principles for stream ciphers.