coinjoin-simulator/run_wasabi_anonymity_study.py at main · joinmarket-ng/coinjoin-simulator · GitHub

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#!/usr/bin/env python3
"""Evaluate the practical anonymity of real Wasabi 2.0 (WabiSabi) rounds.

Scores the per-output anonymity of cached Wasabi rounds
(``data/wasabi2_rounds_enriched.json``, produced by ``fetch_wasabi_rounds.py``)
on the Diaz et al. degree and Serjantov-Danezis effective set, under a *trusted*
coordinator with its deterministic decomposition and ignoring tagging/sybil
side channels (the generous, structural reading). Writes
``wasabi_anonymity_results.json``.

Usage::

    python fetch_wasabi_rounds.py --rounds 80     # refresh the dataset first
    python run_wasabi_anonymity_study.py
"""

from __future__ import annotations

import json
import time
from dataclasses import asdict
from pathlib import Path

from coinjoin_simulator.wasabi_anonymity import build_wasabi_study

OUTPUT_PATH = Path("wasabi_anonymity_results.json")


def main() -> None:
    study = build_wasabi_study()
    if study.n_rounds == 0:
        print(
            "No Wasabi rounds found. Run `python fetch_wasabi_rounds.py` first "
            "to populate data/wasabi2_rounds_enriched.json."
        )
        return

    payload = {
        "generated_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
        "threat_model": (
            "trusted coordinator + deterministic decomposition; tagging, "
            "address-reuse, timing, and sybil side channels ignored (a generous "
            "structural upper bound on Wasabi 2.0 output anonymity)"
        ),
        "n_rounds": study.n_rounds,
        "total_standard_outputs": study.total_standard_outputs,
        "total_nonstandard_outputs": study.total_nonstandard_outputs,
        "standard_value_sats": study.standard_value_sats,
        "nonstandard_value_sats": study.nonstandard_value_sats,
        "nonstandard_value_share": study.nonstandard_value_share,
        "mean_naive_set": study.mean_naive_set,
        "mean_effective_set": study.mean_effective_set,
        "mean_degree": study.mean_degree,
        "mean_degree_given_candidates": study.mean_degree_given_candidates,
        "median_effective_set": study.median_effective_set,
        "median_round_median_effective_set": study.median_round_median_effective_set,
        "round_weighted_mean_effective_set": study.round_weighted_mean_effective_set,
        "round_weighted_mean_degree": study.round_weighted_mean_degree,
        "round_weighted_deanonymized_fraction": study.round_weighted_deanonymized_fraction,
        "value_weighted_mean_effective_set": study.value_weighted_mean_effective_set,
        "value_weighted_mean_degree": study.value_weighted_mean_degree,
        "mean_bayes_vulnerability": study.mean_bayes_vulnerability,
        "mean_worst_case_effective_set": study.mean_worst_case_effective_set,
        "deanonymized_fraction": study.deanonymized_fraction,
        "amount_bins": [asdict(b) for b in study.amount_bins],
        "denomination_summary": [asdict(b) for b in study.denomination_summary],
        "rounds": [asdict(r) for r in study.rounds],
    }
    OUTPUT_PATH.write_text(json.dumps(payload, indent=2) + "\n")
    print(f"Wrote {OUTPUT_PATH}\n")

    print(f"Wasabi 2.0 anonymity over {study.n_rounds} real rounds "
          f"({study.total_standard_outputs} standard outputs):")
    print(f"  mean same-denomination set (Wasabi number): {study.mean_naive_set:.1f}")
    print(f"  mean effective set (Diaz/Serjantov-Danezis): {study.mean_effective_set:.1f}")
    print(f"  mean Diaz degree vs same-denom N:            {study.mean_degree:.3f}")
    print(
        "  mean degree given feasible candidates:       "
        f"{study.mean_degree_given_candidates:.3f}"
    )
    print(f"  median effective set:                        {study.median_effective_set:.1f}")
    print(
        "  round-weighted mean effective set:           "
        f"{study.round_weighted_mean_effective_set:.1f}"
    )
    print(f"  nonstandard output value share:              {study.nonstandard_value_share:.1%}")
    print(f"  deanonymized output fraction:                {study.deanonymized_fraction:.3f}")
    print(
        "\nNote: this is the generous structural bound (trusted coordinator, no "
        "side channels). Wasabi 2.0's real weaknesses are the central coordinator "
        "and the absence of sybil-cost (fidelity-bond) protection, which this "
        "number deliberately does not penalize."
    )


if __name__ == "__main__":
    main()