Universal Gaussian Elimination Hardware for Cryptographic Purposes

In this paper, we investigate the possibility of performing Gaussian elimination for arbitrary binary matrices on hardware. In particular, we presented a generic approach for hardware-based Gaussian elimination, which is able to process both non-singular and singular matrices. Previous works on hardware-based Gaussian elimination can only process non-singular ones. However, a plethora of cryptosystems, for instance, quantum-safe key encapsulation mechanisms based on rank-metric codes, ROLLO and RQC, which are among NIST post-quantum cryptography standardization round-2 candidates, require performing Gaussian elimination for random matrices regardless of the singularity. We accordingly implemented an optimized and parameterized Gaussian eliminator for (singular) matrices over binary fields, making the intense computation of linear algebra feasible and efficient on hardware. To the best of our knowledge, this work solves for the first time eliminating a singular matrix on reconfigurable hardware and also describes the a generic hardware architecture for rank-code based cryptographic schemes. The experimental results suggest hardware-based Gaussian elimination can be done in linear time regardless of the matrix type

FPGA-based key generator for the Niederreiter cryptosystem using binary goppa codes

This paper presents a post-quantum secure, efficient, and tunable FPGA implementation of the key-generation algorithm for the Niederreiter cryptosystem using binary Goppa codes. Our key-generator implementation requires as few as 896,052 cycles to produce both public and private portions of a key, and can achieve an estimated frequency Fmax of over 240 MHz when synthesized for Stratix V FPGAs. To the best of our knowledge, this work is the first hardware-based implementation that works with parameters equivalent to, or exceeding, the recommended 128-bit “post-quantum security” level. The key generator can produce a key pair for parameters m=13, t=119, and n=6960 in only 3.7 ms when no systemization failure occurs, and in 3.5⋅3.7 ms on average. To achieve such performance, we implemented an optimized and parameterized Gaussian systemizer for matrix systemization, which works for any large-sized matrix over any binary field GF(2m). Our work also presents an FPGA-based implementation of the Gao-Mateer additive FFT, which only takes about 1000 clock cycles to finish the evaluation of a degree-119 polynomial at 213 data points. The Verilog HDL code of our key generator is parameterized and partly code-generated using Python and Sage. It can be synthesized for different parameters, not just the ones shown in this paper. We tested the design using a Sage reference implementation, iVerilog simulation, and on real FPGA hardware