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

EAGER：SaTC：用于车辆边缘系统中协作感知的隐私保护卷积神经网络

• 批准号: 2037982
• 负责人: Qing Yang
• 金额: $ 9.99万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037982&HistoricalAwards=false
• 关键词: EAGER; SaTC; Privacy; Preserving; Convolutional

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.

协作感知使车辆能够相互交换传感器数据，以实现协作对象检测和分类，这对于增强自动驾驶车辆的安全性非常有用，因为它们经常遭受盲点和/或遮挡。然而，如果没有适当的数据保护措施，由于担心潜在的隐私泄露，很少有车辆可能分享其传感器数据。车辆可以在与他人共享之前对其传感器数据进行加密，然而，数据加密仅在传输期间保护数据安全性，即，接收车辆在解密数据之后仍然可以访问私有和敏感信息。为了解决这个问题，设计了一种保护隐私的卷积神经网络（CNN），以便可以融合和处理车辆生成的加密传感器数据，以产生基于密文的对象检测结果。虽然接收车辆从接收到的密文中获得有意义的对象检测结果，但它无法恢复原始数据，从而保护了与其他车辆共享的数据的隐私。该技术是一种通用的解决方案，可以扩展为其他机器学习方法提供数据隐私保护。该项目将具有变革性，因为它将使车辆能够在彼此之间安全地共享有用的传感器数据。这种进步对于扩大自动驾驶汽车的视线和视野非常有益，从而确保公共安全，促进智能交通和国家繁荣。该项目提供了各种各样的研究活动，从CNN分析，安全算法设计和硬件编程。研究人员将积极吸引具有各种背景的学生参与该项目。将作出特别努力，增加代表性不足的学生研究人员的参与。每年举办一次关于互联与自动驾驶汽车的研讨会，将该项目的研究成果推广到工业和研究界，造福社会。该项目的目标是了解实现自动驾驶汽车之间的合作感知的安全和隐私挑战，然后设计一个有效和高效的隐私保护CNN来处理多辆汽车共享的加密传感器数据。利用加性秘密共享技术，传感器数据首先被随机分割并加密成两个密文，然后由两个非合谋的边缘服务器以加密格式进行处理。为了处理加密的数据，边缘服务器使用保护隐私的CNN模型，该模型安全地实现了原始CNN中的所有层，包括安全卷积层、安全激活层、安全池化层、安全区域提议网络层和安全全连接层。由于边缘服务器仅拥有传感器数据的部分视图，因此数据中包含的敏感信息受到保护。为了优化隐私保护CNN模型，设计了跨层优化和基于现场可编程门阵列（FPGA）的加速技术，以提高目标检测和分类的速度，同时保持低能耗。拟议的系统将作为使用CNN进行数据隐私保护的令人信服的概念验证，从而为在处理传感器数据时广泛采用隐私保护CNN打开了大门。该项目的源代码和隐私保护CNN模型，以及沿着的训练和测试数据集将被公开，作为推动车辆边缘系统数据隐私保护创新研究的催化剂。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

MLCNN: Cross-Layer Cooperative Optimization and Accelerator Architecture for Speeding Up Deep Learning Applications

MLCNN：用于加速深度学习应用的跨层协作优化和加速器架构

• 发表时间: 2022
• 期刊: 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS
• 作者: Beilei Jiang, Xianwei Cheng

APCNN: Explore Multi-Layer Cooperation for CNN Optimization and Acceleration on FPGA

APCNN：探索多层合作在 FPGA 上实现 CNN 优化和加速

• DOI: 10.1145/3431920.3439461
• 发表时间: 2021
• 期刊: The 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
• 作者: Jiang, Beilei;Cheng, Xianwei;Tang, Sihai;Ma, Xu;Gu, Zhaochen;Zhao, Hui;Fu, Song
• 通讯作者: Fu, Song

Distributed Data-Sharing Consensus in Cooperative Perception of Autonomous Vehicles

• DOI: 10.1109/icdcs54860.2022.00119
• 发表时间: 2022-07
• 期刊: 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)
• 作者: Chenxi Qiu;Sourabh Yadav;A. Squicciarini;Qing Yang;Song Fu;Juanjuan Zhao;Chengzhong Xu

Privacy-Preserving Object Detection with Secure Convolutional Neural Networks for Vehicular Edge Computing

使用用于车辆边缘计算的安全卷积神经网络进行隐私保护对象检测

• DOI: 10.3390/fi14110316
• 发表时间: 2022
• 期刊: Future Internet
• 影响因子: 3.4
• 作者: Bai, Tianyu;Fu, Song;Yang, Qing
• 通讯作者: Yang, Qing

PlateGuard: License Plate Privacy Protection for Internet of Vehicles

PlateGuard：车联网车牌隐私保护

• DOI: 10.1109/metrocad51599.2021.00015
• 发表时间: 2021
• 期刊: 2021 Fourth International Conference on Connected and Autonomous Driving (MetroCAD
• 作者: Nutt, Michael;Yang, Qing;Fu, Song
• 通讯作者: Fu, Song