PODI: A Private Object Detection Inference framework for autonomous vehicles

Abstract

Object detection is essential for autonomous vehicles to perceive and understand their environment. The restricted storage and processing capacities of vehicles necessitate the outsourcing of object detection services. However, this may raise concerns regarding the privacy of the uploaded images. Although there have been some studies on privacy-preserving object detection networks, they either lack location privacy protection or involve excessive computational and communication overheads. To address this issue, we propose a private object detection inference framework (PODI), which is based on a Faster R-CNN and aims to protect both classification and location privacy. PODI employs additive secret sharing protocols to support collaborative computation between two edge servers. By using efficient protocols such as secure Maxpool, secure array access, and secure exponent, PODI significantly reduces computational and communication overheads. A theoretical analysis has confirmed the security, correctness, and efficiency of PODI. Extensive experiments were used to demonstrate its security, inference accuracy comparable to plaintext approaches, and lower cost of secure inference.