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

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

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


OA Adeboye

T Dargahi

M Babaie

C Yu


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.


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,

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
Publisher URL
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