量子聚类框架在保护隐私的同时提升分析性能，适用于医疗、安全等敏感数据场景。
Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasets
- 利用对称性量子电路与差分隐私结合，实现参数高效且隐私保障的聚类。
- 在NSL-KDD数据集上达79.3%准确率，成员推理攻击成功率降至38.3%（ε=1.0）。
- 适合处理医疗、网络安全等异构敏感数据，兼具隐私安全与实用性能。
隐私保护聚类在医疗、网络安全和企业应用中至关重要，需在数据保密性与分析性能间取得平衡。本文提出等变量子聚类（EQC），一种参数高效的框架，将对称性感知的量子电路与差分隐私结合，优化隐私-效用权衡。EQC采用p4m等变参数共享机制，在降低电路复杂度的同时保留有效特征表示。在三个隐私敏感数据集——NSL-KDD、CERT Insider Threat v6.2及合成的MIMIC-III临床数据集上进行评估。在NSL-KDD基准测试中，当隐私预算ε = 1.0、δ = 10^-5时，EQC实现79.3%聚类准确率，成员推理攻击成功率降至38.3%，优于代表性经典与量子基线方法。消融实验表明，性能提升主要源于参数高效的电路设计与差分隐私的协同作用。结果证明，EQC为跨异构敏感数据集提供了一种可实用的量子就绪隐私保护聚类方案。
原文摘要 · Abstract (English)
Privacy-preserving clustering is critical for analyzing sensitive data in healthcare, cybersecurity, and enterprise applications, where maintaining data confidentiality must be balanced with analytical performance. This paper presents Equivariant Quantum Clustering (EQC), a parameter-efficient framework that integrates symmetry-aware quantum circuits with differential privacy to improve the privacy-utility tradeoff. EQC employs p4m equivariant parameter sharing to reduce circuit complexity while preserving informative feature representations. The framework is evaluated on three privacy-sensitive datasets: NSL-KDD, CERT Insider Threat v6.2, and a synthetic MIMIC-III clinical dataset. On the NSL-KDD benchmark, EQC achieves 79.3% clustering accuracy while reducing membership inference attack success to 38.3% under a privacy budget of ε = 1.0 and δ = 10^-5, outperforming representative classical and quantum baselines. Ablation studies indicate that the performance gains primarily arise from parameter-efficient circuit design combined with differential privacy. The results demonstrate that EQC provides a practical quantum-ready framework for secure and privacy-preserving clustering across heterogeneous sensitive datasets.
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