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e9b79ce90a
Two gaps left by the previous review. Section 3.4 quoted the attribution bracket at N=20,000 while the only committed evidence carrying those columns was the timing run at N=2,000, so a reader diffing report against data saw different numbers for the same quantity. Added configs/attribution.yaml and a make target: it records both bounds and the graph hop distance at the reported scale, cheaply, since the adversary and deanonymization metrics are closed-form and the hop distance is a property of the topology. It reproduces the section exactly -- L = 2.58 and neighbourhood confidence 0.640 at degree 8, f_adv 0.2. It also surfaces a result the smaller run could not: degree cuts both ways. A sparser graph has longer routes, so it offers the adversary more upstream places to see the message -- L is 4.18 at degree 4 against 1.93 at degree 16, lifting neighbourhood confidence from 0.61 to 0.72. The low diameter that makes propagation fast also starves the adversary, one of the few places where raising the degree helps anonymity rather than hurting it. Section 3.11 was the only section without a figure. Fig 25 plots MAP success against the effective anonymity set for both release designs: the dashed sets separate far faster than the solid best-guess curves, which is the whole argument for not trusting perplexity alone. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
64 lines
2.2 KiB
Makefile
64 lines
2.2 KiB
Makefile
VENV ?= .venv
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PY := $(VENV)/bin/python
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STAMP := $(VENV)/.installed
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# Keep numpy/scipy BLAS single-threaded so joblib process parallelism doesn't oversubscribe.
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export OMP_NUM_THREADS := 1
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export OPENBLAS_NUM_THREADS := 1
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export MKL_NUM_THREADS := 1
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export NUMEXPR_NUM_THREADS := 1
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.PHONY: install smoke sweep sweep-fullscale redundancy percolation correlated-churn cover-traffic timing attribution figures verify test lint clean
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# The stamp is the real install; targets below depend on it so `make sweep` (etc.) auto-installs
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# on a fresh checkout and re-installs whenever pyproject.toml changes.
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$(STAMP): pyproject.toml
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python3 -m venv $(VENV)
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$(PY) -m pip install -U pip
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$(PY) -m pip install -e ".[dev]"
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@touch $(STAMP)
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install: $(STAMP)
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smoke: $(STAMP) ## fast end-to-end (seconds): tiny N, few rounds/seeds
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$(PY) -m blend.sweep --config configs/smoke.yaml
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sweep: $(STAMP)
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$(PY) -m blend.sweep --config configs/default.yaml
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sweep-fullscale: $(STAMP)
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$(PY) -m blend.sweep --config configs/fullscale.yaml
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redundancy: $(STAMP) ## messaging redundancy R=1..4 (delivery vs deanonymization, time-to-link)
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$(PY) -m blend.sweep --config configs/redundancy.yaml
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percolation: $(STAMP) ## churn threshold: coverage collapse at u_c = 1 - 1/(degree-1)
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$(PY) -m blend.sweep --config configs/percolation.yaml
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attribution: $(STAMP) ## attribution confidence: the local bound and the neighbourhood bound
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$(PY) -m blend.sweep --config configs/attribution.yaml
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cover-traffic: $(STAMP) ## cover traffic: blending, mixing, and the emission-quota ceiling
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$(PY) -m blend.sweep --config configs/cover-traffic.yaml
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timing: $(STAMP) ## release designs under a timing attack (jitter vs clock tick)
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$(PY) -m blend.sweep --config configs/timing.yaml
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correlated-churn: $(STAMP) ## correlated AS/region outages vs uniform churn, matched fractions
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$(PY) -m blend.sweep --config configs/correlated-churn.yaml
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figures: $(STAMP) ## make figures RUN=runs/<dir>
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$(PY) -m blend.plotting.make_figures --run $(RUN)
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verify: $(STAMP)
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$(PY) -m blend.verify
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test: $(STAMP)
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$(PY) -m pytest
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lint: $(STAMP)
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$(VENV)/bin/ruff check src scripts tests
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clean:
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rm -rf runs/* figures/* .pytest_cache .ruff_cache .mypy_cache
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