研究隐私保护下社区发现的精度代价，给出理论保障。
On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models
- 基于边差分隐私设计私有谱聚类算法
- 揭示隐私预算与社区标签恢复准确率的权衡关系
- 适用于需保护数据隐私的社交网络分析场景
我们研究随机块模型（SBMs）中面向社区检测的隐私保护谱聚类。重点关注边差分隐私（DP），提出用于社区恢复的私有算法。工作探讨隐私预算与社区标签准确恢复之间的根本权衡，并建立信息论条件以保证方法的准确性，在边差分隐私下提供成功社区恢复的理论保证。
原文摘要 · Abstract (English)
We investigate privacy-preserving spectral clustering for community detection within stochastic block models (SBMs). Specifically, we focus on edge differential privacy (DP) and propose private algorithms for community recovery. Our work explores the fundamental trade-offs between the privacy budget and the accurate recovery of community labels. Furthermore, we establish information-theoretic conditions that guarantee the accuracy of our methods, providing theoretical assurances for successful community recovery under edge DP.
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