Privacy-aware adversarial defense with explainable AI for adversarial robustness in AI model

AI models in critical sectors such as healthcare and finance must provide both data privacy and adversarial robustness. Differ-ential Privacy (DP) protects training data by injecting noise, but this noise smooths decision boundaries and leaves models opento adversarial evasion, a tension known as the Privacy-Robustness Trade-off. Although this trade-off is well documented, itsinternal mechanism remains underexplored: prior work does not reveal how the noise reshapes a model’s reasoning or whichfeatures become vulnerable. To close this gap, we propose a Privacy-Aware Adversarial Defense grounded in ExplainableAI. Specifically, we introduce the Attention Concentration Score (ACS), which measures how DP training shifts Transformerattention away from task-critical features toward non-functional ones. This attention drift correlates with adversarial vul-nerability, providing a mechanistic explanation of the trade-off. Building on this insight, we develop a Manifold-AlignedSemantic Attack that targets the most drifted features, and a TrustScore defense that fuses embedding-level anomaly detec-tion with attention-level consistency checks. We validate across two datasets (Adult Census, MIMIC-IV), two architectures(DeBERTa-V3-Large, LLaMA−3.1-8B), and seven experiments benchmarking five attacks against six defenses. Within therecommended range (epsilon ∈ [5, 10]), models retain 84.2% accuracy (96.2% of baseline), while TrustScore reaches an Area Underthe ROC Curve (AUC) of 0.87-−0.94, outperforming Isolation Forest (0.65) and supervised detection (0.58). Moreover, theseconclusions hold under feature-categorization variants (attribution- and PCA-based); privacy noise disproportionately desta-bilizes the minority class; the consistency signal adds sub-millisecond overhead; and a surrogate-attention variant preservesdetection under black-box deployment, establishing the approach’s dependability for reliable intelligent environments.