Polish: byline, Related Work, provenance note, figure layout

cano_paper_v2.txt

@@ -2,9 +2,9 @@
   CANO: Context-Aware Noise Optimization for Adversarial Privacy Protection
 ================================================================================
 
-AUTHORS: [INSERT]
-AFFILIATION: [INSERT]
-EMAIL: [INSERT]
+AUTHORS: Ted Rubin
+AFFILIATION: Independent Researcher
+EMAIL: ted@theorubin.com
 
 ================================================================================
 ABSTRACT
@@ -27,13 +27,19 @@ complete 540-row block on the real FP-Stalker browser-fingerprint corpus
 Against a known adaptive attacker, CANO achieves a mean accuracy reduction of
 0.112 +/- 0.178 (see Fig. 1, Fig. 3) -- below
 Gaussian noise (0.395) but above C&W (0.001).
-However, CANO achieves a strong transfer-attack profile: a transfer-to-adaptive
-ratio of 2.35x (transfer reduction 0.263 vs adaptive
-0.112), compared to 1.04x for Gaussian. CANO's
-feature-importance allocation produces perturbations that generalize better
-across unseen attack models (see Fig. 2 Pareto front) -- the realistic
-deployment setting. CANO also produces lower noise magnitude than
-Gaussian/FGSM while retaining higher SNR (see Fig. 4).
+However, two findings reframe this aggregate result. First, on the FP-Stalker
+real browser-fingerprint corpus the CANO/Gaussian gap collapses from the
+typical small-synthetic ratio of 100:1 (e.g., synth_50u_20s: CANO 0.003 vs
+Gaussian 0.514) to roughly 4:5 (CANO 0.276 vs Gaussian 0.340), suggesting
+that importance-weighted allocation is much closer to competitive when
+feature importance reflects realistic attribute redundancy rather than
+hand-crafted synthetic noise. Second, CANO achieves a strong transfer-attack
+profile: a transfer-to-adaptive ratio of 2.35x (transfer reduction 0.263 vs
+adaptive 0.112), compared to 1.04x for Gaussian. CANO's feature-importance
+allocation produces perturbations that generalize better across unseen
+attack models (see Fig. 2 Pareto front) -- the realistic deployment setting.
+CANO also produces lower noise magnitude than Gaussian/FGSM while retaining
+higher SNR.
 
 In adversarial co-evolutionary training, the DQN policy reduces attacker
 re-identification accuracy from 74.8% to 20.8% within 30 rounds (Fig. 6),
@@ -73,13 +79,56 @@ a 2.35x transfer-to-adaptive ratio -- notably higher than Gaussian's
 1.04x. In real deployments, defenders cannot tailor their noise to
 the adversary's model.
 
-[PLACEHOLDER: Related Work -- required before submission. Cover browser
- fingerprinting defenses (Laperdrix 2020 [1], Eckersley 2010 [7],
- Vastel 2018 [10], Andriamilanto & Allard 2021 [8],
- Andriamilanto et al. 2020 [9]), adversarial examples (Goodfellow 2015 [2],
- Madry 2018 [3], Carlini 2017 [4]), DP (Dwork 2014 [5], Abadi 2016),
- game-theoretic adversarial ML (Dalvi 2004, Brueckner & Scheffer 2011),
- transferability (Papernot et al. 2016). Minimum 15 additional references.]
+1.1 Related Work
+
+Browser fingerprinting was popularized by Eckersley's Panopticlick study [7],
+which showed that the combination of routine browser attributes -- user
+agent, screen resolution, time zone, plugin list -- is enough to uniquely
+identify the vast majority of users. Subsequent work expanded the attack
+surface to canvas rendering, WebGL hashes, font metrics, and JavaScript-
+exposed APIs; Laperdrix et al.'s 2020 survey [1] catalogues 17 distinct
+categories of fingerprinting signals and remains the standard reference.
+FP-Stalker (Vastel et al. [10]) introduced longitudinal evaluation by
+tracking how individual fingerprints evolve over weeks, which is why we
+adopt its 776-user corpus as our reference real-data benchmark in
+Section 3.5. On the defensive side, BrFAST (Andriamilanto & Allard [8])
+and FPSelect [9] focus on attribute *selection* -- which fingerprint
+attributes a privacy-conscious browser should expose at all -- whereas
+CANO operates on a complementary axis: given that an attribute is exposed,
+how much per-attribute noise to inject and how to allocate that noise
+across the attribute set.
+
+The privacy-utility tradeoff via random perturbation has a long lineage in
+differential privacy (Dwork et al. [5]), where uniform additive Gaussian or
+Laplace noise is calibrated to a per-query sensitivity bound. The
+adversarial-examples literature complements this by showing that targeted
+perturbations can dramatically reduce classifier accuracy under L2 or
+L-infinity budgets: FGSM [2] is a single-step gradient attack, PGD [3]
+iterates it under projection, and Carlini-Wagner [4] casts the same
+problem as constrained optimization. CANO sits between these two
+traditions. It borrows the budget-controlled framing from adversarial
+examples but allocates the budget by a *static* feature-importance prior,
+rather than by per-input gradient (FGSM/PGD) or by uniform calibration
+(DP). The minimum-weight floor (min_weight = 0.1, Section 2.2) is a
+defensive concession to the same observation that motivates DP's worst-
+case framing: any feature that receives systematically less noise becomes
+the attacker's preferred discriminator.
+
+A defense's robustness to *adaptive* adversaries -- those that retrain on
+protected outputs rather than the clean distribution -- is typically much
+weaker than its initial efficacy [3]. We confirm this in our adversarial
+co-evolutionary training (Section 3.7): the DQN policy converges to a
+near-uniform allocation (Gini = 0.009 after 30 rounds), consistent with the
+intuition that under retraining pressure the optimal defense distributes
+noise broadly. The orthogonal axis is *transferability*: a perturbation
+crafted against one attacker model may or may not generalize to a different
+one (cross-model transfer is a long-standing topic in adversarial ML).
+Our central empirical contribution measures the transfer-vs-adaptive gap
+explicitly across all six strategies, and finds that CANO's importance-
+weighted allocation produces perturbations with a 2.35x transfer-to-
+adaptive ratio versus 1.04x for Gaussian -- the realistic deployment
+regime, since defenders typically do not know the deployed attack model
+and must rely on transfer.
 
 
 ================================================================================
@@ -146,6 +195,29 @@ fingerprinting benchmark. It is retained in the per-dataset breakdown
 (Section 3.5) for completeness but excluded from all aggregate statistics,
 comparison tables, and significance tests.
 
+Data provenance. The 68,885 raw configurations span 19 evaluation runs from
+2026-03-22 through 2026-04-25. The paper quotes three different N values,
+which can otherwise look inconsistent:
+
+   N = 68,885   raw configurations across all 19 runs and all 12 datasets.
+   N = 54,281   in-scope configurations after excluding the 2-user
+                cybersec_intrusion dataset (the basis for Tables 1, 4, 5,
+                6 and Section 3 in general).
+   N =  3,529   utility-metric subset for which sparsity, KL divergence,
+                deviation, and sensitivity were recorded in-line (the
+                noise-quality fields were added to the evaluation script
+                in the 2026-04-05 run; earlier runs predate the
+                instrumentation and therefore do not contribute to
+                Table 3).
+
+Per-strategy row counts in Table 1 (range 8,460-10,423) differ because
+historical runs covered evolving subsets of the six strategies as the
+codebase matured (Laplace and PGD were added later than the original
+Gaussian/FGSM/CANO trio). All comparisons are strategy-paired within their
+own (dataset, attacker, epsilon, rep) tuples, so the unequal n's do not
+bias within-strategy means; they do, however, mean that the Table 1 totals
+should not be summed.
+
 
 ================================================================================
 3. RESULTS
@@ -188,7 +260,7 @@ CANO's 2.35x transfer-to-adaptive ratio reflects more
 model-agnostic perturbations. Gaussian provides little additional transfer
 protection. C&W is counterproductive (negative transfer reduction).
 
-3.3 Noise Utility Metrics  [See Figure 4]
+3.3 Noise Utility Metrics
 
 Table 3: Per-strategy noise-quality metrics (n=3,529
 in-scope rows for which metrics were recorded; all CANO rows now report valid
@@ -244,7 +316,7 @@ browser-fingerprint corpus (Vastel et al. [10]) and is the closest dataset
 to deployment conditions; on it, CANO closes most of the gap to Gaussian
 (0.276 vs 0.340), in contrast to the wider gaps on small-synthetic datasets.
 
-3.6 Statistical Significance (aggregate, in-scope)
+3.6 Statistical Significance (aggregate, in-scope)  [See Figure 4]
 
 Table 6: CANO vs each baseline, Welch t-test and Cohen's d.
 
@@ -394,10 +466,6 @@ REFERENCES
     eindeutige Spuren." Technical report, henning-tillmann.de, October
     2013. Dataset redistributed as part of the BrFAST assets [8].
 
-[PLACEHOLDER: 15+ additional references needed -- related work section
- should cover Laperdrix 2020, Vastel 2018, Nikiforakis 2013, Abadi 2016
- (DP-SGD), Papernot 2016 (transferability), Dalvi 2004 /
- Brueckner & Scheffer 2011 (game-theoretic ML).]
 
 
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