Can I read Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions on EtoBox?

Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions by Xie, Chulin; Cao, Zhong; Long, Yunhui; Yang, Diange; Zhao, Ding; Li, Bo is a scholarly article available to read on EtoBox.

What is Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions about?

Recent advances in machine learning have enabled its wide application in different domains, and one of the most exciting applications is autonomous vehicles (AVs), which have encouraged the development of a number of ML algorithms from perception to prediction to planning. However, training AVs usually requires a large amount of training data collected from different driving environments (e.g., cities) as well as different types of personal information (e.g., working hours and routes). Such collected large data, treated as the new oil for ML in the data-centric AI era, usually contains a large amount of privacy-sensitive information which is hard to remove or even audit. Although existing privacy protection approaches have achieved certain theoretical and empirical success, there is still a gap when applying them to real-world applications such as autonomous vehicles. For instance, when training AVs, not only can individually identifiable information reveal privacy-sensitive information, but also population-level information such as road construction within a city, and proprietary-level commercial secrets of AVs. Thus, it is critical to revisit the frontier of privacy risks and cor

Author

Xie, Chulin; Cao, Zhong; Long, Yunhui; Yang, Diange; Zhao, Ding; Li, Bo

Published

2022

Language

EN