README.md

---
license: cc-by-sa-4.0
language:
  - en
size_categories:
  - 10K<n<100K
tags:
  - reinforcement-learning
  - browser-fingerprinting
  - adversarial-machine-learning
  - differential-privacy
  - transfer-attacks
pretty_name: CANO Adversarial Privacy Evaluations
---

# CANO Adversarial Privacy Evaluations

68,885 experimental evaluations of six noise-injection privacy strategies
(CANO, Gaussian, FGSM, PGD, Laplace, Carlini-Wagner) against three adaptive
attacker models (Random Forest, Gradient Boosting, MLP) across 12 datasets,
including the real **FP-Stalker** browser-fingerprint corpus
(776 users, 13,674 fingerprints, 34 attributes; Vastel et al., IEEE S&P 2018).

Aggregate statistics in the paper are computed over 54,281 in-scope
configurations after excluding the 2-user `cybersec_intrusion` dataset
(binary task, not a k-class fingerprinting benchmark).

## Files

| File | Description |
|---|---|
| `cano_evaluations.csv` | Per-config raw results: strategy, epsilon, attacker, rep, dataset, accuracy_reduction, transfer_reduction, noise L2/SNR/sparsity, KL divergence, sensitivity, etc. |
| `cano_paper_v2.{txt,pdf}` | Paper draft with refreshed tables and the FP-Stalker findings. |
| `evaluation_results.json` | Aggregate strategy comparison, per-dataset best, significance tests, RL training summary. |
| `feature_importance.json` | Permutation importance from the Phase 2 Random Forest (used as CANO's allocation prior). |
| `rl_optimization.json` | DQN policy training trajectory (30 rounds, 50 users, Gini convergence). |

## Key findings

1. **Transfer-attack profile**: CANO achieves a 2.35× transfer-to-adaptive
   ratio versus 1.04× for Gaussian — feature-importance allocation produces
   more model-agnostic perturbations, which is the realistic deployment
   setting (the defender can't tailor noise to the attacker's exact model).

2. **Real-world generalization**: On the FP-Stalker corpus, the CANO/Gaussian
   gap narrows substantially (0.276 vs 0.340 mean accuracy reduction)
   compared to small-synthetic datasets (e.g., synth_50u_20s: 0.003 vs 0.514).
   Importance-weighted allocation generalizes better when feature importance
   reflects real attribute redundancy rather than synthetic noise.

3. **Game-theoretic equilibrium**: RL-trained noise allocation converges
   to near-uniform (Gini = 0.009) — uniform noise is the equilibrium against
   adaptive adversaries, challenging static feature-weighted defense
   assumptions.

## Citation

See `cano_paper_v2.txt` / `cano_paper_v2.pdf` for the methodology, full
results tables, and references. The companion code lives at
[github.com/tedrubin80/Adversarial-Privacy](https://github.com/tedrubin80/Adversarial-Privacy).