CRSF: Collusion-Resilient Privacy-Preserving Sensor Fusion with Byzantine-Robust Participation

2026-09-01

AI Summary

本文提出CRSF协议，通过PBFT和阈值保护等方法解决隐私保护传感器融合中的共谋及拜占庭问题，保证安全性和性能。

Abstract

Privacy-preserving sensor fusion enables an untrusted server to compute an aggregate result over distributed sensor measurements without learning either individual inputs or the final output. Recent garbled-circuit-based protocols provide an efficient realization of this functionality in a sensor--server--client architecture, but remain vulnerable to sensor--server collusion and Byzantine manipulation of sensor participation. These weaknesses can compromise honest-sensor privacy, incorrectly exclude honest sensors, and corrupt the computed fusion result, thereby undermining the security guarantees expected from the protocol. We present CRSF, a collusion-resilient sensor-fusion protocol that addresses these weaknesses while providing privacy, correctness with explicit abort, and liveness. CRSF introduces a Practical Byzantine Fault Tolerance (PBFT)-based agreement phase for sensor submissions and uses server-specific, status-dependent label release with threshold protection of circuit-input labels. This design prevents any Byzantine server from unilaterally manipulating sensor participation and prevents any admissible sensor-server coalition from obtaining enough secret material to compromise honest-sensor privacy. We implement CRSF and compare its online execution time with the most relevant state-of-the-art baseline. Our Google Cloud evaluation measures the total computation and communication cost of the online protocol under fault-free and representative faulty executions. Across a range of fault-tolerant fusion circuits and up to 261 sensors, CRSF demonstrates a highly practical trade-off between robust security and protocol performance.

Problem

• privacy-preserving sensor fusion
• sensor-server collusion
• Byzantine manipulation

Innovation

• Collusion-Resilient
• Byzantine-Robust
• Sensor Fusion
• Privacy-Preserving
• PBFT