Sharing The Secret: Distributed Privacy-Preserving Monitoring

2026-03-20

AI Summary

This work addresses the limitations of traditional runtime monitoring systems, which rely on a single monitor and heavyweight cryptographic techniques, thereby struggling to balance privacy preservation with real-time scalability. The paper proposes the first secret-sharing protocol supporting continuous monitoring, replacing conventional encryption with a distributed multi-party architecture that assumes at least one honest participant. This approach ensures strong privacy guarantees while maintaining a dynamic internal state. Implemented within the MP-SPDZ framework, the system demonstrates significantly reduced computational overhead and outperforms existing solutions in both scalability and performance, making it well-suited for real-time monitoring applications.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Multiagent Systems: Mechanism Design

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

In traditional runtime verification, a system is typically observed by a monolithic monitor. Enforcing privacy in such settings is computationally expensive, as it necessitates heavy cryptographic primitives. Therefore, privacy-preserving monitoring remains impractical for real-time applications. In this work, we address this scalability challenge by distributing the monitor across multiple parties -- at least one of which is honest. This architecture enables the use of efficient secret-sharing schemes instead of computationally intensive cryptography, dramatically reducing over-head while maintaining strong privacy guarantees. While existing secret-sharing approaches are typically limited to one-shot executions which do not maintain an internal state, we introduce a protocol tailored for continuous monitoring that supports repeated evaluations over an evolving internal state (kept secret from the system and the monitoring entities). We implement our approach using the MP-SPDZ framework. Our experiments demonstrate that, under these architectural assumptions, our protocol is significantly more scalable than existing alternatives.

Problem

• privacy-preserving monitoring
• runtime verification
• secret sharing
• distributed monitoring
• scalability

Innovation

• distributed monitoring
• privacy-preserving
• secret sharing
• runtime verification
• continuous evaluation