EAGER: SaTC: Privacy-Preserving Convolutional Neural Network for Cooperative Perception in Vehicular Edge Systems

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Cooperative perception enables vehicles to exchange sensor data among each other to achieve collaborative object detection and classification, which is extremely useful to enhance autonomous vehicles' safety as they frequently suffer from blind spots and/or occlusions. Without proper measures in place for data protection, however, very few vehicles are likely to share their sensor data, due to the concerns on potential privacy leakage. A vehicle can encrypt its sensor data before sharing it with others, however, data encryption only protects data security during transmission, i.e., a receiving vehicle can still access private and sensitive information after it decrypts the data. To address this issue, a privacy-preserving convolutional neural network (CNN) is designed so that encrypted sensor data generated by vehicles can be fused and processed to produce ciphertext-based object detection results. Although a receiving vehicle obtains meaningful object detection results from received ciphertexts, it has no means to recover the original data, thus protecting the privacy of data shared from other vehicles. The proposed technique is a generic solution that can be extended to offer data privacy protection for other machine learning methods. This project will be transformative because it will enable vehicles to securely share useful sensor data between each other. Such an advance can be extremely beneficial for extending the line of sight and field of view of autonomous vehicles, which secures the public safety and advances smart transportation and national prosperity. This project offers a wide variety of research activities, ranging from CNN analysis, security algorithm design, and hardware programming. The investigators will actively engage students with various backgrounds in this project. Special efforts will be made to increase the participation of underrepresented student researchers. A workshop on Connected and Autonomous Vehicles will be organized annually to promote the research outcomes from this project to the industry and research communities and benefit society.The goal of this project is to understand the security and privacy challenges in achieving cooperative perception among autonomous vehicles, and then design an effective and efficient privacy-preserving CNN for processing encrypted sensor data, shared from multiple vehicles. Leveraging the additive secret sharing technique, sensor data is first randomly split and encrypted into two ciphertexts, and then processed in the encrypted format by two non-colluding edge servers. To process the encrypted data, edge servers make use of a privacy-preserving CNN model that securely implements all layers in the original CNN, including the secure convolutional layer, secure activation layer, secure pooling layer, secure region proposal network layer, and secure full-connection layer. As an edge server possesses only a partial view of the sensor data, sensitive information contained in the data is protected. To optimize the privacy-preserving CNN model, cross-layer optimization and field programmable gate arrays (FPGA)-based acceleration techniques are designed to increase the speed of object detection and classification while keeping the energy consumption low. The proposed system will serve as a convincing proof-of-concept for data privacy protection using CNNs, thus opening the door to widespread adoption of privacy-preserving CNN in processing sensor data. The source code and the privacy-preserving CNN model, along with its training and testing datasets, will be made publicly available, serving as a catalyst for enabling innovative research on data privacy protection in vehicular edge systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.