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 that apply identical perturbations across all features, 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.