partial_sum_optimization

Programs

pso.py

Optimize the partial sum guessing order

Usage:

pso.py [-h] [-s [STEPS]] tweakey_setting final_round start_round tweakey_cell balanced_cell input_active scale

• tweakey_setting: Specify version of Skinny used
• final_round: Final round of the key recovery
• start_round: Start round of the key recovery
• tweakey_cell: Specify which tweakey cell is controlled by the attacker
• balanced_cell: Specify the balanced cell from the output of the distinguisher
• input_active: Specify how many cell are active at the input of the distinguisher
• scale: Scale the time complexity, must be used since many solver only support limited data types
• s [STEPS], --steps [STEPS] Specify the maximum number of steps, default are the involved subtweakey cells

Example: python pso.py 1 18 15 5 4 9 32

psvisu.py

Visualize the partial sum recovery steps

Usage:

psvisu.py [-h] [-c] [-s] [-n] [-m] [-p] input [input ...]

• -c, --color Use different color for every step
• -s, --steps Visualize each step individually
• -n, --step-number Put the step number in each cell except the stk
• -m, --memory Mark which states have to be stored in memory
• -p, --pdf Run latex and output pdf
• input: json file with key guess order (output of autopsy2)

Example:

python psvisu.py 1_18_15_5_4_9.json -n -p

or

python psvisu.py 1_18_15_5_4_9.json -s -m -p

Output:

Partial Sum Key Recovery Visualization stored in 3_25_17_14_1_4.pdf

Compiling the file requires the various cipher *.sty files in the top-level directory. If using latexmk to compile, this path is included automatically by the .latexmkrc file; otherwise, please copy to the same directory as the tex file.