DeepClean : a robust deep learning technique for autonomous vehicle camera data privacy

Adeboye, OA; Dargahi, T; Babaie, M; Saraee, MH; Yu, C

Abstract

Autonomous Vehicles (AVs) are equipped with several sensors which produce various forms of data, such as
geo-location, distance, and camera data. The volume and utility of these data, especially camera data, have
contributed to the advancement of high-performance self-driving applications. However, these vehicles and
their collected data are prone to security and privacy attacks. One of the main attacks against AV-generated
camera data is location inference, in which camera data is used to extract knowledge for tracking the users. A
few research studies have proposed privacy-preserving approaches for analysing AV-generated camera data
using powerful generative models, such as Variational Auto Encoder (VAE) and Generative Adversarial
Network (GAN). However, the related work considers a weak geo-localisation attack model, which leads
to weak privacy protection against stronger attack models. This paper proposes DeepClean, a robust deeplearning
model that combines VAE and a private clustering technique. DeepClean learns distinct labelled
object structures of the image data as clusters and generates a more visual representation of the non-private
object clusters, e.g., roads. It then distorts the private object areas using a private Gaussian Mixture Model
(GMM) to learn distinct cluster structures of the labelled object areas. The synthetic images generated
from our model guarantee privacy and resist a robust location inference attack by less than 4% localisation
accuracy. This result implies that using DeepClean for synthetic data generation makes it less likely for a
subject to be localised by an attacker, even when using a robust geo-localisation attack. The overall image
utility level of the generated synthetic images by DeepClean is comparable to the benchmark studies.

Citation

Adeboye, O., Dargahi, T., Babaie, M., Saraee, M., & Yu, C. (2022). DeepClean : a robust deep learning technique for autonomous vehicle camera data privacy. IEEE Access, https://doi.org/10.1109/ACCESS.2022.3222834

• Journal Article Type: Article
• Acceptance Date: Jul 11, 2022
• Publication Date: Nov 17, 2022
• Deposit Date: Nov 28, 2022
• Publicly Available Date: Nov 28, 2022
• Journal: IEEE Access
• Publisher: Institute of Electrical and Electronics Engineers
• DOI: https://doi.org/10.1109/ACCESS.2022.3222834
• Publisher URL: https://doi.org/10.1109/ACCESS.2022.3222834
• Related Public URLs: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639