We present the first practically realizable side-channel assisted fault attack on any block-ciphers having bit-permutation with optimal diffusion, that can retrieve the round key efficiently using random nibble faults. The attack demonstrates how side-channel leakage can allow the adversary to precisely determine the fault mask resulting from a nibble fault injection instance. We first demonstrate the viability of such attack model via side-channel analysis experiments on top of a laser-based fault injection setup, targeting a PRESENT-80 and GIFT-128 (two popular block-ciphers based on bit-permutation having optimal diffusion) implementation on an ATmega328P microcontroller. Subsequently, we present a differential fault analysis (DFA) exploiting the knowledge of the output fault mask in the target round to recover multiple last round keys nibbles independently and in parallel. We show that the combined attack can recover the last round key of PRESENT-80 and GIFT-128 with 4 random nibble fault injections in the best case. In the average case, the number of random nibble faults required for PRESENT-80 and GIFT-128 are 9-18 and 6-9 respectively.

Designing cryptographically good and power-efficient 4 × 4 S-boxes is a challenging problem in the era of lightweight cryptography. Although the optimal cryptographic properties are easy to determine, verifying the power efficiency of an S-box is nontrivial. The conventional approach of determining the power consumption using commercially available CAD tools is highly time-consuming, which becomes formidable while dealing with a large pool of S-boxes. This mandates the development of automation that should quickly characterize the power efficiency from the Boolean function representation of an S-box. In this paper, we present a supervised machine-learning-assisted automated framework to resolve the problem for 4 × 4 S-boxes, which turns out to be 14 times faster than the traditional approach. The key idea is to extrapolate the knowledge of literal counts, AND-OR-NOT gate counts in the sum-of-products (SOP) form of the underlying Boolean functions to predict the dynamic power efficiency. We demonstrate the effectiveness of our framework by reporting on a set of power-efficient (involutive) optimal S-boxes from a large set of S-boxes. We also develop a deterministic model using results obtained from supervised learning to predict the dynamic power of an S-box that can be used in an evolutionary algorithm to generate cryptographically good and low-power S-boxes.

Authenticated encryption schemes which resist misuse of nonce at some desired level of privacy are two-pass or Mac-then-Encrypt constructions (inherently inefficient but provide full privacy) and online constructions like McOE, sponge-type authenticated encryptions (such as duplex) and COPA. Only the last one is almost parallelizable except that for associated data processing, the final block-cipher call is sequential (it needs to wait for the encryption of all the previous ones). In this paper, we design a new online secure authenticated encryption, called ELmD or Encrypt-Linear mix-Decrypt, which is completely (two-stage) parallel (even in associated data) and fully pipeline implementable. It also provides full privacy when associated data is not repeated. Like COPA, our construction is based on EME, an Encrypt-Mix-Encrypt type SPRP construction (secure against chosen plaintext and ciphertext). But unlike EME, we have used an online computable efficient linear mixing instead of a non-linear mixing. We have also provided the hardware implementation of the construction and compare the performance with similar constructions like COPA and EME2.