Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption
Researchers introduce Sarus, a framework that leverages homomorphic encryption to enable privacy-preserving fusion of perception outputs from multiple autonomous vehicle vendors. The system aggregates encrypted detection data, protecting both proprietary model details and sensitive environmental information. Experiments on the KITTI dataset demonstrate that Sarus achieves improved scene-level coverage by combining complementary detections from different modalities, with linear computational scaling and bounded overhead, indicating feasibility for real-time deployment.
Why it matters: This work demonstrates a practical approach to secure, privacy-preserving cooperative perception in autonomous vehicles, addressing key barriers to multi-vendor collaboration without exposing sensitive data.
Full story at: arXiv Cryptography and Security ↗