"""Generate the LaTeX rows of every result table from the CSVs, so that every table in the paper is reproducible from data/ (TIFS mandate). Prints the tabular bodies; paste into main.tex without edits. """ from __future__ import annotations import csv import json import math from pathlib import Path DATA = Path(__file__).resolve().parents[1] / "data" NAME = { "proposed": r"\textbf{Proposed keyed masking}", "public_mask": "Public masks", "perm_key": r"Permutation key~\cite{chen2023shuffling}", "index_cipher": "Index cipher", "oma_plain": "OMA (no encryption)", "random": "Random", "hadamard": "Walsh-Hadamard", "learned": "Learned", "learned_reg": r"Regularized~\eqref{eq:regloss}", } RECEIVER = { "legit": "Legitimate", "oma": "OMA", "insider": "Insider", "eve": "Outsider", } def f3(x: str) -> str: """Three decimals, or an em-dash for a value that does not apply.""" try: v = float(x) except (TypeError, ValueError): return "--" return "--" if math.isnan(v) else f"{v:.3f}" def cell(x: str, bold: bool) -> str: s = f3(x) if s == "--": return "--" return rf"$\mathbf{{{s}}}$" if bold else f"${s}$" def compare_table(): print("% Table: scheme comparison (from sec_compare.csv)") rows = list(csv.DictReader(open(DATA / "sec_compare.csv"))) order = ["public_mask", "perm_key", "index_cipher", "oma_plain", "proposed"] rows.sort(key=lambda r: order.index(r["scheme"])) # stage_E does not jam the orthogonal reference, because the jammer an # OMA user faces is targeted at public slots rather than mask-matched # or mask-blind. stage_L measures that case, so the cell comes from # there instead of being left empty. jam = {float(r["jsr_db"]): r for r in csv.DictReader(open(DATA / "sec_jam_cmp.csv"))} oma_jam = jam[0.0]["oma_targeted"] for r in rows: b = r["scheme"] == "proposed" if r["scheme"] == "oma_plain" and f3(r["jam0_ser"]) == "--": r["jam0_ser"] = oma_jam cells = [cell(r[k], b) for k in ("legit_ser", "eve_out", "eve_in", "jam0_ser")] print(f"{NAME[r['scheme']]} & " + " & ".join(cells) + r" \\") def maskfam_table(): print("% Table: key families (from sec_maskfam.csv)") for r in csv.DictReader(open(DATA / "sec_maskfam.csv")): cells = [cell(r[k], False) for k in ("legit_ser", "eve_ser", "eve_ones_ser", "mask_xcorr")] print(f"{NAME[r['family']]} & " + " & ".join(cells) + r" \\") def real_table(): print("% Table: headline recovery (from real_sec_stats.json)") st = json.loads((DATA / "real_sec_stats.json").read_text()) rec = st["recovery"] snrs = sorted(rec, key=float) for key in ("legit", "oma", "insider", "eve"): cells = " & ".join(f"${rec[s][key]:.3f}$" for s in snrs) print(f"{RECEIVER[key]} & {cells}" + r" \\") def refresh_tables(): print("% Table: key refresh (from refresh_summary.csv)") for r in csv.DictReader(open(DATA / "refresh_summary.csv")): b = r["scheme"] == "Invariant" name = r"\textbf{Invariant}" if b else r["scheme"] f = (lambda t: r"\mathbf{" + t + "}") if b else (lambda t: t) print(f"{name} & ${f(format(float(r['legit']), '.3f'))}$ & " f"${f(format(float(r['eve']), '.4f'))}$ & " f"${f(format(float(r['entropy_bits']), '.1f'))}$~bits" + r" \\") print() print("% Table: known plaintext across a refresh (from refresh_kpa.csv)") rows = {r["n_frames"]: r for r in csv.DictReader(open(DATA / "refresh_kpa.csv"))} keep = ["2", "8", "64"] print("Frames used by the attacker & " + " & ".join(f"${k}$" for k in keep) + r" \\") for lbl, key in (("Same block", "ser_same_block"), ("Next block", "ser_next_block")): print(f"{lbl} & " + " & ".join(f"${float(rows[k][key]):.3f}$" for k in keep) + r" \\") if __name__ == "__main__": compare_table(); print() maskfam_table(); print() real_table(); print() refresh_tables()