CRFLMar 20

Sharing The Secret: Distributed Privacy-Preserving Monitoring

arXiv:2603.2010749.0h-index: 105
AI Analysis

This work addresses the problem of making privacy-preserving monitoring practical for real-time applications, representing an incremental improvement over existing secret-sharing approaches by supporting continuous monitoring with internal state.

The paper tackles the scalability challenge of privacy-preserving runtime verification by distributing the monitor across multiple parties, enabling the use of efficient secret-sharing schemes instead of heavy cryptography, which dramatically reduces overhead while maintaining strong privacy guarantees.

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.

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