梳理26种安全混洗协议，揭示如何选对隐私保护方案
How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations
- 系统整理26种混洗协议并统一安全标准
- 指出现有实现存在性能与漏洞隐患
- 适合隐私计算研究者和工程落地参考
Ishai 等人（FOCS'06）首次提出安全混洗作为隐私数据聚合的高效组件。近年来，差分隐私领域重新关注安全混洗，因其在多种计算中可提供隐私放大效应。尽管多项工作强调其价值，但常将其视为黑箱，忽视现有实现中的实际漏洞与性能权衡。这引出核心问题：何为优质安全混洗？本综述通过识别、分类并对比26种实现必要混洗功能的协议，回答该问题。为支持有意义比较，我们统一并适配现有安全定义为一致属性集。同时，概述依赖安全混洗的隐私保护技术，提供协议选型实用指南，并指出未来研究方向。
原文摘要 · Abstract (English)
Ishai et al. (FOCS'06) introduced secure shuffling as an efficient building block for private data aggregation. Recently, the field of differential privacy has revived interest in secure shufflers by highlighting the privacy amplification they can provide in various computations. Although several works argue for the utility of secure shufflers, they often treat them as black boxes; overlooking the practical vulnerabilities and performance trade-offs of existing implementations. This leaves a central question open: what makes a good secure shuffler? This survey addresses that question by identifying, categorizing, and comparing 26 secure protocols that realize the necessary shuffling functionality. To enable a meaningful comparison, we adapt and unify existing security definitions into a consistent set of properties. We also present an overview of privacy-preserving technologies that rely on secure shufflers, offer practical guidelines for selecting appropriate protocols, and outline promising directions for future work.
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