How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations

2025-07-02

AI Summary

Prior research on secure shufflers often treats protocols as black boxes, lacking unified security definitions and systematic evaluation—leading to a disconnect between theory and practice. Method: This paper presents the first systematic survey of 26 secure shuffling protocols, proposing a unified security attribute model encompassing correctness, privacy (resistance to differential attacks and input tracing), robustness, and efficiency, along with a comparable evaluation framework. Leveraging formal cryptographic protocol analysis, secure multi-party computation, and differential privacy theory, we conduct a horizontal assessment across privacy-preserving data aggregation scenarios, measuring security guarantees, communication/computation overhead, and scalability. Contribution/Results: Our analysis uncovers critical trade-offs among performance metrics and deployment risks, yielding an application-oriented protocol selection guide. This work significantly advances the practical adoption of secure shuffling within privacy amplification mechanisms.

Technology Category

• Machine Learning: Privacy
• Philosophy and Ethics of AI: Privacy & Security
• Multiagent Systems: Mechanism Design

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Survey identifies and compares 26 secure shuffling protocols
• Examines practical vulnerabilities in secure shuffler implementations
• Provides guidelines for selecting privacy-preserving shuffling protocols

Innovation

• Survey compares 26 secure shuffling protocols
• Unifies security definitions for consistent evaluation
• Provides guidelines for selecting shuffling protocols