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

## TL;DR

This paper introduces a new method for anonymizing real-time data streams that improves privacy while minimizing information loss and latency.

## Contribution

A delay-aware anonymization framework combining k-anonymity and l-diversity with dynamic reconfiguration strategies for real-time data streams.

## Key findings

- Stricter privacy can coexist with lower distortion when using moderate-to-high k and sufficient delay budgets.
- The proposed framework reduces reconfigurations and information loss compared to k-only methods.
- Privacy metrics like l-satisfaction rate and entropy improve with the new approach.

## Abstract

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.