Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption

Published

July 21, 2026

Author(s)

Munawar Hasan, Apostol Vassilev

Abstract

Cooperative perception enables autonomous vehicles (AVs) to improve situational awareness by aggregating detection outputs from multiple agents and sensing platforms, often via a shared fusion service in multi-vendor deployments. However, sharing such outputs at inference time exposes proprietary model behavior and sensitive environmental information, creating significant privacy and security concerns. In this paper, we present Sarus, a privacy-preserving framework for multi-vendor perception fusion via homomorphic encryption (HE), enabling aggregation without revealing individual vendor outputs. Each vendor encodes detections as compact Gaussian moment vectors over a shared spatial lattice and transmits encrypted payloads to a fusion server, which aggregates them directly in the encrypted domain. The fused result is then decrypted and reconstructed into final detections through class-wise bin merging. We analyze the computational complexity, showing linear scaling for vendor payload construction and O(BV) server-side fusion with the number of occupied bins B and vendors V, while postprocessing scales as O(B+∑c∈CB2c), where C denotes the set of object classes and Bc is the number of occupied bins for class c. Experiments demonstrate linear scaling in practice with only a bounded constant-factor overhead from HE, with decryption dominating postprocessing cost. Experiments on the KITTI dataset using camera (YOLOv8) and LiDAR (PointPillars, PV-RCNN) detectors show that Sarus improves scene-level coverage by effectively aggregating complementary detections, particularly in distance-dependent regimes where individual modalities degrade. These results indicate that privacy-preserving multi-vendor perception fusion is feasible for real-time deployment when statistical compression and spatial sparsity are jointly exploited.

Citation

arXiv.org

Pub Weblink

https://arxiv.org/abs/2607.19146

Pub Type

Websites

Download Paper

https://doi.org/10.48550/arXiv.2607.19146

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Keywords

cooperative perception, privacy-preserving inference, multi-vendor fusion, autonomous vehicles, homomorphic encryption, secure aggregation

Citation

Hasan, M. and Vassilev, A. (2026), Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption, arXiv.org, [online], https://doi.org/10.48550/arXiv.2607.19146, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=962301, https://arxiv.org/abs/2607.19146 (Accessed July 25, 2026)

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