具有差分隐私的等变量子聚类：跨异构敏感数据集的参数高效隐私保护分析
Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasets
AI总结：
研究如何在隐私保护下对异构敏感数据集进行聚类分析，提出等变量子聚类框架EQC，结合对称感知量子电路与差分隐私，采用p4m等变参数共享降复杂度，实验表明其在多个数据集上表现良好，为隐私保护聚类提供实用框架。
摘要：
隐私保护聚类对于医疗保健、网络安全和企业应用中的敏感数据分析至关重要，在这些应用中，必须在保持数据机密性和分析性能之间取得平衡。本文提出了等变量子聚类（EQC），这是一个参数高效的框架，它将对称感知量子电路与差分隐私相结合，以改善隐私-效用权衡。EQC采用p4m等变参数共享来降低电路复杂性，同时保留信息特征表示。该框架在三个隐私敏感数据集上进行了评估：NSL-KDD、CERT Insider Threat v6.2和合成的MIMIC-III临床数据集。在NSL-KDD基准上，EQC在隐私预算为{\epsilon}=1.0和{\delta}=10^-5的情况下，实现了79.3%的聚类准确率，同时将成员推理攻击成功率降低到38.3%，优于代表性的经典和量子基线。消融研究表明，性能提升主要源于参数高效的电路设计与差分隐私的结合。结果表明，EQC为跨异构敏感数据集的安全和隐私保护聚类提供了一个实用的量子就绪框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
