This repository accompanies the paper Multi-moduli NTTs for Saber on Cortex-M3 and Cortex-M4 available at https://tches.iacr.org/index.php/TCHES/article/view/9292/8858.

Authors:
- Amin Abdulrahman amin.abdulrahman@rub.de
- Jiun-Peng Chen jpchen@citi.sinica.edu.tw
- Yu-Jia Chen yujia@email.ikv- tech.com.tw
- Vincent Hwang vincentvbh7@gmail.com
- Matthias J. Kannwischer <matthias@kannwischer.eu>
- Bo-Yin Yang <by@crypto.tw>

In this repository, we provide code for unmasked Saber on Cortex-M3 and Cortex-M4 and masked Saber on Cortex-M4. Please go to libopencm3, and type make -j4 first.
If you acquire this package by downloading the artifact, then this package contains all files required.
If you are github user, please clone with

git clone --recursive https://github.com/multi-moduli-ntt-saber/multi-moduli-ntt-saber.git

Our contribution is listed as follows.

• Unmasked Saber.
  • Cortex-M3. We provide four implementations. Three of them are reported in our paper and the newly included one is claimed in our paper.
    • m3speed: 16-bit NTT approach with strategy A for KeyGen, Encap, and Decap. This is the fastest approach for Saber on Cortex-M3.
    • m3speedstack: 16-bit NTT approach with strategy B for KeyGen, and strategy C for Encap and Decap. This is the one claimed in our paper and we provide the benchmarks now.
    • m3stack: 16-bit NTT approach with strategy D for KeyGen, Encap, and Decap. This is the implementation with smallest stack usage for Saber on Cortex-M3.
    • m3_32bit: 32-bit NTT approach with strategy A for KeyGen, Encap, and Decap.
  • Cortex-M4. We provide two implementations. All of them are reported in our paper.
    • m4fspeed: 32-bit NTT approach with strategy A for KeyGen, Encap, and Decap. This is the fastest approach for Saber on Cortex-M4.
    • m4fstack: Our multi-moduli approach achieving a new record on the stack usage on Cortex-M4. This is the implementation with the smallest stack usage for Saber on Cortex-M4.
• Masked Saber. Our multiplier is a generic multiplier for Saber and can be used for any masking order. However, we only provide experinments with the first-order masked Saber. The non-linear parts are from https://eprint.iacr.org/2020/733.
  • Cortex-M4. We provide three implementations and only focus on the decapsulation for this part. All of them are multi-moduli approaches reported in our paper.
    • m4speed: Strategy A. This is the fastest approach for masked Saber on Cortex-M4.
    • m4speedstack: Strategy C.
    • m4stack: Strategy D. This is the implementations with the smallest stack usage for Saber on Cortex-M4.

Additionally, we also provide C functions that are used for developing the assembly code and generating the tables.

Structure of this repository

• unmasked contains our implementations for unmasked Saber.
• masked contains our implementations for masked Saber.
• gen_table contains the files for generating the tables.
• examples contains examples about how we use the code in gen_table for developing assembly code.

Requirements

• arm-none-eabi-gcc with version 10.2.0
• python3 with pyserial and numpy
• For Cortex-M3:
  • Board nucleo-f207zg
  • openocd version 0.11.0, see https://openocd.org/openocd-0-11-0-released.html
• For Cortex-M4F:
  • Board stm32f4discovery (we use floating-point registers)
  • stlink

libopencm3

The libopencm3 in this repository is the commit 6763681c260cf280487d70ca0d1996a8b79fff30 of https://github.com/libopencm3/libopencm3.

License

Several masked non-linear functions for masked Saber in the folder masked are covered by MIT license by https://github.com/KULeuven-COSIC/SABER-masking. The MIT license is added at the beginning of the files. libopencm3 is licensed under GPL version 3, see https://github.com/libopencm3/libopencm3.

All remaining code is covered by CC0.