International Association
for Cryptologic Research

We present and evaluate a custom extension to the RISC-V instruction set for finite field arithmetic. The result serves as a very compact approach to software-hardware co-design of PQC implementations in the context of small embedded processors such as smartcards. The extension provides instructions that implement finite field operations with subsequent reduction of the result. As small finite fields are used in various PQC schemes, such instructions can provide a considerable speedup for an otherwise software-based implementation. Furthermore, we create a prototype implementation of the presented instructions for the extendable VexRiscv core, integrate the result into a chip design, and evaluate the design on two different FPGA platforms. The effectiveness of the extension is evaluated by using the instructions to optimize the Kyber and NewHope key-encapsulation schemes. To that end, we also present an optimized software implementation for the standard RISC-V instruction set for the polynomial arithmetic underlying those schemes, which serves as basis for comparison. Both variants are tuned on an assembler level to optimally use the processor pipelines of contemporary RISC-V CPUs. The result shows a speedup for the polynomial arithmetic of up to 85% over the basic software implementation. Using the custom instructions drastically reduces the code and data size of the implementation without introducing runtime-performance penalties at a small cost in circuit size. When used in the selected schemes, the custom instructions can be used to replace a full general purpose multiplier to achieve very compact implementations.

This paper presents an attack based on side-channel information and information set decoding (ISD) on the code-based Niederreiter cryptosystem and an evaluation of the practicality of the attack using an electromagnetic side channel. We start by directly adapting the timing side-channel plaintext-recovery attack by Shoufan et al. from 2010 to the constant-time implementation of the Niederreiter cryptosystem as used in the official FPGA-implementation of the NIST finalist “Classic McEliece”. We then enhance our attack using ISD and a new technique that we call iterative chunking to further significantly reduce the number of required side-channel measurements. We theoretically show that our attack improvements have a significant impact on reducing the number of required side-channel measurements. For example, for the 256-bit security parameter set kem/mceliece6960119 of “Classic McEliece”, we improve the basic attack that requires 5415 measurements to less than 562 measurements on average to mount a successful plaintext-recovery attack. Further reductions can be achieved at the price of increasing the cost of the ISD computations. We confirm our findings by practically mounting the attack on the official FPGA-implementation of “Classic McEliece” for all proposed parameter sets.

The current state of the art of Boolean masking for the modular addition operation in software has a very high performance overhead. Firstly, the instruction count is very high compared to a normal addition operation. Secondly, until recently, the entropy consumed by such protections was also quite high. Our paper significantly improves both aspects, by applying the Threshold Implementation (TI) methodology with two shares and by reusing internal values as randomness source in such a way that the uniformity is always preserved. Our approach performs considerably faster compared to the previously known masked addition and subtraction algorithms by Coron et al. and Biryukov et al. improving the state of the art by 36%, if we only consider the number of ARM assembly instructions. Furthermore, similar to the masked adder from Biryukov et al. we reduce the amount of randomness and only require one bit additional entroy per addition, which is a good trade-off for the improved performance. We applied our improved masked adder to ChaCha20, for which we provide two new first-order protected implementations and achieve a 36% improvement over the best published result for ChaCha20 using an ARM Cortex-M4 microprocessor.