CANO: Context-Aware Noise Optimization for Adversarial Privacy Protection
We additionally train a Deep Q-Network (DQN) policy through adversarial co-evolutionary training, where the defense adapts against an attacker that continuously retrains on protected data. We evaluate CANO against five baseline strategies (Gaussian, FGSM, PGD, Carlini-Wagner, and Laplace noise) across 12 datasets (including the real FP-Stalker browser-fingerprint corpus — 776 users, 13,674 fingerprints), 3 attack models, and 6 noise budgets, totaling 68,885 experimental configurations (54,281 in-scope after excluding the 2-user cybersec_intrusion dataset).
