Real-Time Stream Data Anonymization via Dynamic Reconfiguration with l-Diversity-Enhanced SUHDSA

Pipelines that satisfy k-anonymity alone remain vulnerable to attribute disclosure under skewed sensitive attributes. We studied real-time anonymization of high-throughput data streams under strict delay budgets (β). We jointly enforced k-anonymity and l-diversity via a delay-aware Monitor-Trigger-Repair controller that selects swap vs. merge by minimizing a weighted objective λΔIL + (1 - λ)ΔRT while bounding overhead with a neighbor cap (c) and a growth cap (γ). On UCI Adult stream replay, we identified operating regions where stricter privacy does not necessarily increase distortion: with moderate-to-high k and sufficiently large β, groups satisfy l preemptively, reducing reconfigurations and avoiding aggressive generalization, thereby mitigating information loss relative to k-only baselines. Privacy metrics (l-satisfaction rate and entropy) also improved. We further report a focused sensitivity analysis on λ, c, and γ and evaluate an entropy-driven adaptive lt controller, showing that these levers provide interpretable trade-offs between latency and distortion and can suppress excessive reconfiguration and tail latency.