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+---
+title: "CANO: Context-Aware Noise Optimization for Adversarial Privacy Protection"
+author: "Ted Rubin"
+affiliation: "Independent Researcher"
+email: "ted@theorubin.com"
+date: "April 2026"
+abstract: |
+  We present **CANO** (Context-Aware Noise Optimization), an adaptive noise
+  injection system that optimizes the privacy-utility tradeoff in adversarial
+  privacy protection. Unlike uniform noise strategies, CANO allocates noise
+  proportionally to each feature's contribution to re-identification,
+  concentrating protection where it matters most while preserving utility on
+  low-impact features.
+  
+  We evaluate CANO against five baseline strategies (Gaussian, FGSM, PGD,
+  Carlini-Wagner, and Laplace) across 68,885 experimental configurations
+  spanning 12 datasets (11 after excluding the 2-user
+  `cybersec_intrusion` dataset from aggregate statistics), 3 attack
+  models, and 6 noise budgets. Aggregate statistics are computed over
+  54,281 in-scope configurations, including a complete block on the
+  real **FP-Stalker** browser-fingerprint corpus (Vastel et al. [10]; 776
+  users, 13,674 fingerprints, 34 attributes).
+  
+  Against a known adaptive attacker, CANO achieves a mean accuracy reduction
+  of 0.112 ± 0.178 -- below
+  Gaussian noise (0.395) but above C&W (0.001).
+  Two findings reframe this aggregate result. First, on the FP-Stalker
+  corpus the CANO/Gaussian gap collapses substantially (CANO 0.276
+  vs Gaussian 0.340), suggesting importance-weighted allocation
+  generalizes better under realistic feature distributions. Second, CANO
+  achieves a 2.41x transfer-to-adaptive ratio versus
+  1.04x for Gaussian -- a substantial advantage in the
+  realistic deployment regime where the defender does not know the attack
+  model.
+  
+  In adversarial co-evolutionary training, the DQN policy reduces attacker
+  re-identification accuracy from 74.8% to 20.8% within
+  30 rounds, converging to near-uniform allocation (Gini:
+  0.009) -- empirically demonstrating that uniform noise is the
+  game-theoretic equilibrium against adaptive adversaries.
+keywords: [privacy protection, adversarial noise, reinforcement learning, browser fingerprinting, transfer attacks]
+---
+
+# 1. Introduction
+
+Browser fingerprinting poses a significant threat to user privacy. Attackers
+construct unique device fingerprints from browser attributes -- canvas
+rendering, WebGL, screen resolution, installed fonts -- to track users across
+sessions without cookies [1].
+
+Privacy-preserving systems combat fingerprinting by injecting noise. A
+fundamental unresolved tension: whether uniform noise injection or
+feature-weighted injection provides superior protection. A further practical
+challenge: privacy systems are typically deployed without knowledge of the
+adversary's exact attack model.
+
+CANO addresses this through:
+
+1. Feature importance analysis.
+2. Proportional noise allocation with a minimum weight floor preventing
+   exploitable zero-noise features.
+3. RL that adapts allocation through adversarial co-evolution.
+4. Empirical analysis of the adaptive-vs-transfer tradeoff.
+
+Our central finding is counterintuitive: while CANO does not maximize accuracy
+reduction against a known adaptive attacker (Gaussian dominates), CANO achieves
+a 2.41x transfer-to-adaptive ratio -- notably higher than
+Gaussian's 1.04x. In real deployments, defenders cannot
+tailor their noise to the adversary's model.
+
+## 1.1 Related Work
+
+Browser fingerprinting was popularized by Eckersley's Panopticlick study [7],
+which showed that combinations of routine browser attributes uniquely identify
+most users. Laperdrix et al.'s 2020 survey [1] catalogues 17 distinct
+categories of fingerprinting signals. FP-Stalker (Vastel et al. [10])
+introduced longitudinal evaluation by tracking fingerprint evolution over
+weeks -- we adopt its 776-user corpus as our real-data benchmark. On the
+defensive side, BrFAST [8] and FPSelect [9] focus on attribute *selection*
+(which attributes to expose), whereas CANO operates on a complementary axis:
+given an attribute is exposed, how much per-attribute noise to inject.
+
+The privacy-utility tradeoff has a long lineage in differential privacy
+(Dwork et al. [5]), where Gaussian or Laplace noise is calibrated to a
+per-query sensitivity bound. The adversarial-examples literature shows
+targeted perturbations can dramatically reduce classifier accuracy: FGSM [2]
+is a single-step gradient attack, PGD [3] iterates it under projection, and
+Carlini-Wagner [4] casts it as constrained optimization. CANO borrows the
+budget-controlled framing from adversarial examples but allocates by a static
+feature-importance prior rather than per-input gradient. The minimum-weight
+floor (§2.2) is a defensive concession to DP's worst-case framing: any
+feature receiving negligible noise becomes the attacker's preferred
+discriminator. Our central empirical contribution measures the
+transfer-vs-adaptive gap explicitly across all six strategies.
+
+# 2. Methodology
+
+## 2.1 Feature Importance Analysis
+
+Random Forest (100 trees) on 1000 fingerprint samples;
+permutation importance (10 repeats). 9-dimensional feature space. Baseline
+re-identification accuracy: 100% on synthetic corpus. Feature importance is
+highly concentrated: feature_0 (0.303) and feature_1 (0.302) account for ~99%
+of total importance; features 2-5 have exactly 0.000 permutation importance.
+This motivates the minimum weight floor ($w_{\min} = 0.1$) in §2.2.
+
+> **Note.** Importance is computed on synthetic proxies, not real browser
+> API measurements. FP-Stalker evaluation uses real per-attribute fingerprints
+> with noise allocated by the same importance weights.
+
+## 2.2 CANO Noise Allocation
+
+Given feature importance weights $w_i$ and noise budget $\epsilon$:
+
+$$\delta_i = \epsilon \cdot (w_i \cdot n) \cdot \operatorname{sign}(z_i)$$
+
+where $z_i \sim \mathcal{N}(0, 1)$, or the gradient direction when a target
+model is available. The $n$ scaling factor ensures equal total noise energy to
+baselines. The minimum-weight floor ($w_{\min} = 0.1$) prevents attackers
+from exploiting negligibly-noised features.
+
+## 2.3 DQN Policy Training
+
+Adversarial co-evolution: 50 simulated users, 20 samples/user, 9 features.
+
+- **State:** [feature_values, attack_confidence, privacy_budget, query_count]
+- **Action:** per-feature noise allocation weights (softmax-normalized)
+- **Reward:** $\alpha \cdot \text{privacy\_gain} - (1 - \alpha) \cdot \text{utility\_cost}$
+
+Training alternates defender (CANO) and attacker (GradientBoosting retraining).
+
+## 2.4 Experimental Setup
+
+- **Strategies:** CANO (ours), Gaussian, FGSM, PGD, Laplace, C&W
+- **Noise budgets:** $\epsilon \in \{0.05, 0.1, 0.15, 0.2, 0.3, 0.5\}$
+- **Attack models:** gradient_boosting, mlp, random_forest
+- **Datasets:** 11 in-scope + `cybersec_intrusion` (excluded, 2-user binary task)
+- **Total configs:** 68,885 raw; 54,281 in-scope
+
+> **Scope note.** `cybersec_intrusion` is retained in Table 5 for completeness
+> but excluded from all aggregate statistics, comparisons, and significance
+> tests.
+
+### Data Provenance
+
+Three N values appear in the paper:
+
+| | Value | Meaning |
+|---|---:|---|
+| $N_{\text{raw}}$ | 68,885 | Raw configurations across all 19 runs and all datasets. |
+| $N_{\text{in-scope}}$ | 54,281 | Excluding `cybersec_intrusion` (basis for Tables 1, 4, 5, 6). |
+| $N_{\text{utility}}$ | 5,924 | Utility-metric subset (sparsity, KL, deviation, sensitivity instrumented from 2026-04-05 onward; basis for Tables 2, 3). |
+
+Per-strategy row counts in Table 1 differ because historical runs covered
+evolving strategy subsets as the codebase matured. All comparisons are
+strategy-paired within (dataset, attacker, epsilon, rep) tuples.
+
+# 3. Results
+
+## 3.1 Overall Strategy Comparison (Adaptive Attack)
+
+![Accuracy reduction vs. noise budget epsilon, per strategy.](results/figures/fig1_accuracy_reduction_vs_epsilon.png)
+
+**Table 1.** Strategy comparison -- aggregate over in-scope datasets only.
+
+| Strategy | Acc. Reduction | Xfer Red. | Noise L2 | SNR (dB) | n |
+|---|---:|---:|---:|---:|---:|
+| CANO (ours) | 0.112 ± 0.178 | +0.271 | 0.435 | 15.5 | 8,508 |
+| Gaussian | 0.395 ± 0.281 | +0.410 | 0.595 | 9.7 | 10,423 |
+| FGSM | 0.212 ± 0.212 | +0.474 | 0.642 | 9.8 | 9,641 |
+| PGD | 0.129 ± 0.171 | +0.145 | 0.271 | 17.5 | 8,789 |
+| Laplace | 0.239 ± 0.222 | +0.392 | 0.556 | 11.2 | 8,460 |
+| C&W | 0.001 ± 0.011 | -0.016 | 0.003 | 53.7 | 8,460 |
+
+Gaussian is the strongest adaptive-attack strategy. CANO outperforms only
+C&W. See §3.6 for significance.
+
+## 3.2 Transfer Attack Analysis
+
+![Privacy-utility Pareto front.](results/figures/fig2_pareto_front.png)
+
+**Table 2.** Adaptive vs. transfer accuracy reduction.
+
+| Strategy | Adaptive | Transfer | Ratio | Gap |
+|---|---:|---:|---:|---:|
+| CANO (ours) | 0.112 | +0.271 | 2.41x | +0.158 |
+| Gaussian | 0.395 | +0.410 | 1.04x | +0.014 |
+| FGSM | 0.212 | +0.474 | 2.23x | +0.261 |
+| PGD | 0.129 | +0.145 | 1.13x | +0.016 |
+| Laplace | 0.239 | +0.392 | 1.64x | +0.153 |
+| C&W | 0.001 | -0.016 | n/a | -0.018 |
+
+CANO's 2.41x transfer-to-adaptive ratio reflects more
+model-agnostic perturbations. Gaussian provides little additional transfer
+protection (1.04x). C&W's transfer reduction is negative
+(anti-protective on small synthetics) -- its ratio is reported as n/a.
+
+> **Note.** Transfer numbers are from the utility-metric subset; the FP-Stalker
+> block does not yet have transfer values computed -- backfill planned.
+
+## 3.3 Noise Utility Metrics
+
+**Table 3.** Per-strategy noise-quality metrics (n = 5,924 in-scope rows).
+
+| Strategy | Sparsity | KL | Deviation | Sensitivity | n |
+|---|---:|---:|---:|---:|---:|
+| CANO (ours) | 0.980 | 0.763 | 0.1763 | -0.226 | 900 |
+| Gaussian | 0.983 | 0.507 | 0.1421 | +0.032 | 1,080 |
+| FGSM | 0.983 | 1.275 | 0.1881 | -0.174 | 1,080 |
+| PGD | 0.834 | 0.299 | 0.0699 | +0.035 | 1,064 |
+| Laplace | 0.980 | 0.545 | 0.1706 | -0.185 | 900 |
+| C&W | 0.980 | 0.021 | 0.0009 | -0.016 | 900 |
+
+CANO's noise structure is distinguishable from Gaussian: similar KL divergence
+but negative sensitivity signature (vs. Gaussian's positive), reflecting
+concentration on importance-ranked rather than variance-ranked features.
+
+## 3.4 Epsilon Sensitivity
+
+![Per-strategy heatmap of accuracy reduction across datasets.](results/figures/fig3_strategy_heatmap.png)
+
+**Table 4.** Accuracy reduction by noise budget (aggregate, in-scope).
+
+| ε | CANO (ours) | Gaussian | FGSM | PGD | Laplace | C&W |
+|---:| ---:| ---:| ---:| ---:| ---:| ---:|
+| 0.05 | 0.010 | 0.104 | 0.054 | 0.018 | 0.013 | 0.001 |
+| 0.10 | 0.035 | 0.195 | 0.103 | 0.037 | 0.060 | 0.001 |
+| 0.15 | 0.084 | 0.312 | 0.183 | 0.048 | 0.152 | 0.001 |
+| 0.20 | 0.131 | 0.431 | 0.258 | 0.072 | 0.253 | 0.001 |
+| 0.30 | 0.188 | 0.609 | 0.317 | 0.178 | 0.382 | 0.001 |
+| 0.50 | 0.226 | 0.750 | 0.374 | 0.433 | 0.576 | 0.002 |
+
+## 3.5 Per-Dataset Analysis
+
+![Per-dataset accuracy reduction (core: real + main synthetic datasets).](results/figures/fig5a_per_dataset_core.png)
+
+![Per-dataset accuracy reduction (supplemental: overlap + keystroke).](results/figures/fig5b_per_dataset_supplemental.png)
+
+**Table 5.** Mean accuracy reduction by dataset.
+
+| Dataset | Users | CANO | Gaussian | FGSM | Laplace | PGD |
+|---|---:|---:|---:|---:|---:|---:|
+| `fpstalker` *(real)* | 776 | 0.276 | 0.340 | 0.282 | 0.291 | 0.176 |
+| `synth_large` | 20 | 0.135 | 0.416 | 0.234 | 0.304 | 0.152 |
+| `synth_medium` | 10 | 0.082 | 0.306 | 0.153 | 0.198 | 0.090 |
+| `synth_small` | 5 | 0.116 | 0.287 | 0.190 | 0.215 | 0.109 |
+| `cybersec_intrusion` *(out-of-scope)* | 2 | 0.034 | 0.192 | 0.094 | 0.122 | 0.088 |
+| `keystroke_cmu_51users` | 51 | n/a | 0.657 | 0.357 | n/a | 0.413 |
+| `overlap_10u_50s` | 10 | 0.046 | 0.307 | 0.247 | n/a | n/a |
+| `overlap_20u_30s` | 20 | 0.041 | 0.218 | 0.187 | n/a | n/a |
+| `synth_10u_50s` | 10 | 0.001 | 0.270 | 0.057 | n/a | n/a |
+| `synth_20u_50s` | 20 | 0.001 | 0.378 | 0.135 | n/a | n/a |
+| `synth_50u_20s` | 50 | 0.003 | 0.514 | 0.356 | n/a | n/a |
+| `synth_5u_30s` | 5 | -0.006 | 0.208 | 0.072 | n/a | n/a |
+
+> **Note.** "n/a" means a strategy was not evaluated on that dataset
+> (backfill planned). FP-Stalker (Vastel et al. [10]) is the closest dataset
+> to deployment conditions; on it, CANO closes most of the gap to Gaussian
+> (0.276 vs 0.340).
+
+## 3.6 Statistical Significance
+
+![Statistical significance of CANO vs each baseline.](results/figures/fig4_statistical_significance.png)
+
+**Table 6.** CANO vs each baseline, Welch t-test and Cohen's d.
+
+| Comparison | Cohen's d | p-value | Significance |
+|---|---:|---:|---:|
+| CANO vs C&W | +0.880 | p < 0.001 | *** |
+| CANO vs FGSM | -0.510 | p < 0.001 | *** |
+| CANO vs Gaussian | -1.202 | p < 0.001 | *** |
+| CANO vs Laplace | -0.631 | p < 0.001 | *** |
+| CANO vs PGD | -0.093 | p < 0.001 | *** |
+
+## 3.7 Adversarial Training Results (DQN Policy)
+
+![DQN adversarial training progress (attacker accuracy vs round).](results/figures/rl_training_progress.png)
+
+DQN policy trained over 30 adversarial rounds with 50 users:
+
+- Baseline attack accuracy: **74.8%**
+- Final attack accuracy: **20.8%**
+- Accuracy reduction: **54.0 percentage points**
+- Noise magnitude: 0.6061
+- DQN training steps: 31,500
+- Final Gini coefficient: **0.009** (near-uniform)
+
+Uniform allocation emerges as the game-theoretic equilibrium against adaptive
+adversaries.
+
+# 4. Discussion
+
+## 4.1 Key Findings
+
+1. Noise scaling (the $n$ multiplier) is the most impactful design choice.
+2. Gaussian is the strongest adaptive-attacker defense (d = -1.20 vs CANO).
+3. CANO achieves a 2.41x transfer/adaptive ratio vs
+   1.04x for Gaussian.
+4. RL equilibrium is uniform allocation (Gini = 0.009).
+5. CANO uses less noise than Gaussian ($L_2$ = 0.435 vs
+   0.595) with higher SNR (15.5 vs
+   9.7 dB).
+6. **On the real FP-Stalker corpus, CANO closes most of the gap to Gaussian**
+   (0.276 vs 0.340), in contrast to wider gaps on
+   small-synthetic datasets. Importance-weighted allocation generalizes better
+   under realistic feature distributions.
+
+## 4.2 Limitations
+
+- One real browser-fingerprint dataset (FP-Stalker, 776 users); larger corpora
+  (HTillmann; BrFAST extended) are the next integration.
+- 9 synthetic features with artificial importance concentration.
+- `cybersec_intrusion` (2 users) excluded from aggregates.
+- Utility metrics on 5,924-row subset; backfill re-run planned.
+- RL at 50 users; architectural changes needed for scaling.
+- Transfer numbers not yet computed for the FP-Stalker block.
+
+## 4.3 Future Work
+
+1. Backfill transfer-attack and noise-utility metrics for the FP-Stalker block.
+2. Extend utility-metric coverage across all historical evaluation runs.
+3. Formal DP guarantees for CANO's allocation mechanism.
+4. Larger RL training (1,000+ users); online policy updates in deployment.
+5. Theoretical analysis of conditions under which feature-weighted noise
+   achieves higher transfer efficiency than uniform noise.
+
+# 5. Conclusion
+
+CANO does not match Gaussian in raw adaptive-attack accuracy reduction
+(0.112 vs 0.395), but achieves
+a 2.41x transfer-to-adaptive ratio -- better model-agnostic
+behavior than Gaussian (1.04x) in the transfer setting,
+which better reflects real-world deployment. On the real FP-Stalker corpus
+the adaptive-attack gap narrows substantially (CANO 0.276 vs Gaussian
+0.340), reinforcing the case that importance-weighted allocation
+generalizes better under realistic conditions.
+
+**Contributions:**
+
+1. *Noise scaling correction:* equal total noise energy while redistributing
+   by importance.
+2. *Transfer efficiency result:* feature-importance weighting produces more
+   model-agnostic perturbations.
+3. *RL equilibrium finding:* uniform noise is the game-theoretic equilibrium
+   against adaptive adversaries (Gini = 0.009 after 30 rounds).
+4. *Real-world validation:* FP-Stalker (776 users, 13,674 fingerprints) shows
+   the synthetic CANO/Gaussian gap narrows substantially under realistic
+   feature distributions.
+
+# References
+
+[1]  Laperdrix, P. et al. "Browser Fingerprinting: A Survey." *ACM CSUR*, 2020.
+
+[2]  Goodfellow, I. et al. "Explaining and Harnessing Adversarial Examples."
+     *ICLR*, 2015.
+
+[3]  Madry, A. et al. "Towards Deep Learning Models Resistant to Adversarial
+     Attacks." *ICLR*, 2018.
+
+[4]  Carlini, N. & Wagner, D. "Towards Evaluating the Robustness of Neural
+     Networks." *IEEE S&P*, 2017.
+
+[5]  Dwork, C. et al. "The Algorithmic Foundations of Differential Privacy."
+     *Foundations and Trends in TCS*, 2014.
+
+[6]  Mnih, V. et al. "Human-level Control through Deep Reinforcement Learning."
+     *Nature*, 2015.
+
+[7]  Eckersley, P. "How Unique Is Your Web Browser?" *PETS*, 2010.
+
+[8]  Andriamilanto, N. and Allard, T. "BrFAST: a Tool to Select Browser
+     Fingerprinting Attributes for Web Authentication." *WWW '21 Companion*,
+     ACM, 2021. DOI: 10.1145/3442442.3458610.
+
+[9]  Andriamilanto, N., Allard, T., and Le Guelvouit, G. "FPSelect:
+     Low-Cost Browser Fingerprints for Mitigating Dictionary Attacks."
+     *ACM CCS*, 2020. DOI: 10.1145/3427228.3427297.
+
+[10] Vastel, A., Laperdrix, P., Rudametkin, W., and Rouvoy, R. "FP-STALKER:
+     Tracking Browser Fingerprint Evolutions." *IEEE S&P*, 2018.
+     DOI: 10.1109/SP.2018.00008.
+
+[11] Tillmann, H. "Browser Fingerprinting: 93% der Nutzer hinterlassen
+     eindeutige Spuren." Technical report, henning-tillmann.de, October 2013.
+
+---
+
+*Generated: 2026-04-26 03:41:25*
+*Data source: 19 merged `eval_*.jsonl` files (68,885 raw configs, 54,281 in-scope after excluding `cybersec_intrusion`).*