Adeboye, O. orcid.org/0000-0002-6225-3383, Dargahi, T. orcid.org/0000-0002-0908-6483, Babaie, M. orcid.org/0000-0002-8480-940X et al. (2 more authors) (2022) DeepClean: A Robust Deep Learning Technique for Autonomous Vehicle Camera Data Privacy. IEEE Access, 10. pp. 124534-124544. ISSN 2169-3536
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 deep-learning 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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | This article is protected by copyright. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Autonomous vehicle, data privacy, data utility, deep clustering, generative model |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Thermofluids, Surfaces & Interfaces (iETSI) (Leeds) |
Funding Information: | Funder Grant number University of Salford SERA55 |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Aug 2023 14:17 |
Last Modified: | 23 Aug 2023 14:17 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/access.2022.3222834 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202644 |
Download
Filename: DeepClean_A_Robust_Deep_Learning_Technique_for_Autonomous_Vehicle_Camera_Data_Privacy.pdf
Licence: CC-BY 4.0