ORCID Profile
0000-0002-3603-6617
Current Organisation
University of Technology Sydney
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Data security and protection | Data and information privacy | Cybersecurity and privacy | Global Information Systems | Computer Software | Computer System Security | Information security management |
Information Processing Services (incl. Data Entry and Capture) |
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2011
Publisher: Elsevier BV
Date: 2017
Publisher: Springer Singapore
Date: 2019
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 06-2014
Publisher: Wiley
Date: 21-12-2016
DOI: 10.1002/WCM.2761
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2008
Publisher: IEEE
Date: 14-06-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2011
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 12-2021
DOI: 10.1109/ITHINGS-GREENCOM-CPSCOM-SMARTDATA-CYBERMATICS53846.2021.00039
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2008
Publisher: IEEE
Date: 06-2020
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: IEEE
Date: 06-2014
Publisher: Wiley
Date: 18-09-2016
DOI: 10.1002/WCM.2636
Publisher: IEEE
Date: 10-2014
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 05-2018
Publisher: Springer Berlin Heidelberg
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Springer International Publishing
Date: 14-10-2016
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 10-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2009
Publisher: IEEE
Date: 10-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: Springer International Publishing
Date: 21-11-2019
Publisher: IEEE
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 10-2013
Publisher: Wiley
Date: 13-12-2023
DOI: 10.1002/CPE.7554
Abstract: Privacy leakage in images attracts increasing concerns these days, as photos uploaded to large social platforms are usually not processed by proper privacy protection mechanisms. Moreover, with advanced artificial intelligence (AI) tools such as deep neural network (DNN), an adversary can detect people's identities and collect other sensitive personal information from images at an unprecedented scale. In this paper, we introduce a novel face image de‐identification framework using adversarial perturbations in the feature space. Manipulating the feature space vector ensures the good transferability of our framework. Moreover, the proposed feature space adversarial perturbation generation algorithm can successfully protect the identity‐related information while ensuring the other attributes remain similar. Finally, we conduct extensive experiments on two face image datasets to evaluate the performance of the proposed method. Our results show that the proposed method can generate real‐looking privacy‐preserving images efficiently. Although our framework has only been tested on two real‐life face image datasets, it can be easily extended to other types of images.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: Springer International Publishing
Date: 21-11-2019
Publisher: Springer International Publishing
Date: 21-11-2019
Publisher: IEEE
Date: 27-10-2020
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 11-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: IEEE
Date: 2008
DOI: 10.1109/CSSE.2008.32
Publisher: IEEE
Date: 11-2011
Publisher: IEEE
Date: 05-2017
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 06-2016
Publisher: IEEE
Date: 10-2014
Publisher: Springer Singapore
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: IEEE
Date: 09-2010
Publisher: Springer Science and Business Media LLC
Date: 13-04-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: IEEE
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: IEEE
Date: 27-10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
Publisher: IEEE
Date: 06-2019
Publisher: Springer International Publishing
Date: 21-11-2019
Publisher: IEEE
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: IEEE
Date: 06-2017
Publisher: IEEE
Date: 05-12-2021
Publisher: IEEE
Date: 09-2010
Publisher: IEEE
Date: 09-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: IEEE
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2018
Publisher: IEEE
Date: 06-2016
Publisher: IEEE
Date: 04-2010
DOI: 10.1109/CMC.2010.114
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2015
Publisher: IEEE
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Publisher: IEEE
Date: 06-2018
Publisher: Springer Science and Business Media LLC
Date: 31-01-2022
Publisher: IEEE
Date: 04-08-2021
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 03-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: IEEE
Date: 12-2012
Publisher: Wiley
Date: 09-08-2022
DOI: 10.1002/CPE.6548
Abstract: The explosive growth of various computer vision technologies generates a tremendous amount of visual data online every day. In addition to bringing convenience and revolutionizing our daily life, image data also reveal a wide range of sensitive information and pose unprecedented privacy leakage risks. Particularly, in the case of photos contain human faces, people can easily access those face images on social media without any consent, and the misuse of personal information could cause serious privacy violation to in iduals. Therefore, it is essential to consider sanitizing people's identity information when using images containing human faces. As a result, there has been rapid development in the area of facial anonymization, also called image de‐identification. However, due to the emergence of numerous deep‐learning based attacks, traditional anonymization methods such as blurring and mosaic are weak and ineffective to protect in idual's privacy in face images. To respond to this challenge, this article proposes a novel de‐identification method that utilizes a deep neural network. The proposed framework encompasses two modules: encoder network and generator network. The encoder transforms a face image into a high‐semantic latent vector of codes, which will be de‐identified according to the differential privacy criterion. The generator leverages the unconditional generative adversarial network to synthesize high‐quality images based on the modified latent codes from the encoder. Extensive experimental results indicate that our proposed model can protect image privacy while keeping the processed image visual realistic.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2014
Publisher: Association for Computing Machinery (ACM)
Date: 05-03-2022
DOI: 10.1145/3436755
Abstract: The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-09-2021
Publisher: IEEE
Date: 14-06-2023
Publisher: IEEE
Date: 12-2012
Publisher: IEEE
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2008
Publisher: IEEE
Date: 03-2010
Publisher: IEEE
Date: 10-2021
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 29-04-2019
Publisher: IEEE
Date: 08-2014
Publisher: IEEE
Date: 29-04-2019
Publisher: Elsevier BV
Date: 05-2023
Publisher: IEEE
Date: 12-2020
Publisher: MDPI AG
Date: 24-12-2020
DOI: 10.3390/S21010058
Abstract: With the development of the Internet of Multimedia Things (IoMT), an increasing amount of image data is collected by various multimedia devices, such as smartphones, cameras, and drones. This massive number of images are widely used in each field of IoMT, which presents substantial challenges for privacy preservation. In this paper, we propose a new image privacy protection framework in an effort to protect the sensitive personal information contained in images collected by IoMT devices. We aim to use deep neural network techniques to identify the privacy-sensitive content in images, and then protect it with the synthetic content generated by generative adversarial networks (GANs) with differential privacy (DP). Our experiment results show that the proposed framework can effectively protect users’ privacy while maintaining image utility.
Publisher: Wiley
Date: 14-12-2019
DOI: 10.1002/CPE.5102
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 05-2009
Publisher: IEEE
Date: 15-06-2022
Publisher: IEEE
Date: 05-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 05-2016
Publisher: IEEE
Date: 12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-1970
Publisher: MDPI AG
Date: 23-02-2020
DOI: 10.3390/S20041222
Abstract: In this paper, we investigated a buffer-aided decode-and-forward (DF) wireless relaying system over fading channels, where the source and relay harvest radio-frequency (RF) energy from a power station for data transmissions. We derived exact expressions for end-to-end throughput considering half-duplex (HD) and full-duplex (FD) relaying schemes. The numerical results illustrate the throughput and energy efficiencies of the relaying schemes under different self-interference (SI) cancellation levels and relay deployment locations. It was demonstrated that throughput-optimal relaying is not necessarily energy efficiency-optimal. The results provide guidance on optimal relaying network deployment and operation under different performance criteria.
Publisher: IEEE
Date: 03-2008
Start Date: 07-2023
End Date: 06-2026
Amount: $638,775.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2020
End Date: 12-2023
Amount: $320,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2023
End Date: 12-2025
Amount: $482,610.00
Funder: Australian Research Council
View Funded Activity