Datasets:

datamatters24
/

cano-adversarial-privacy

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

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).
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.
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.