ORCID Profile
0000-0002-5102-7073
Current Organisation
University of Melbourne
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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.
Calculus of Variations, Systems Theory and Control Theory | Applied Mathematics |
Expanding Knowledge in Engineering | Expanding Knowledge in the Mathematical Sciences
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 10-2013
Publisher: arXiv
Date: 2019
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2013
DOI: 10.1137/110860112
Publisher: Elsevier BV
Date: 10-2017
Publisher: Institution of Engineering and Technology (IET)
Date: 07-2017
Publisher: arXiv
Date: 2017
Publisher: Elsevier BV
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: IEEE
Date: 07-2015
Publisher: arXiv
Date: 2021
Publisher: IEEE
Date: 31-05-2023
Publisher: Elsevier BV
Date: 07-2017
Publisher: arXiv
Date: 2013
Publisher: Informa UK Limited
Date: 28-06-2020
Publisher: arXiv
Date: 2019
Publisher: arXiv
Date: 2015
Publisher: arXiv
Date: 2011
Publisher: arXiv
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: Elsevier BV
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: IEEE
Date: 24-03-2021
Publisher: arXiv
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institution of Engineering and Technology (IET)
Date: 11-2020
Publisher: Springer Netherlands
Date: 16-10-2010
Publisher: arXiv
Date: 2017
Publisher: arXiv
Date: 2016
Publisher: Elsevier BV
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Publisher: Springer Netherlands
Date: 11-11-2014
Publisher: IEEE
Date: 12-2013
Publisher: arXiv
Date: 2012
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2014
Publisher: Elsevier BV
Date: 09-2021
Publisher: arXiv
Date: 2011
Publisher: IEEE
Date: 06-2020
Publisher: arXiv
Date: 2021
Publisher: Elsevier BV
Date: 2020
Publisher: arXiv
Date: 2012
Publisher: arXiv
Date: 2015
Publisher: Institution of Engineering and Technology (IET)
Date: 10-2020
Publisher: arXiv
Date: 2013
Publisher: Elsevier BV
Date: 02-2007
Publisher: IEEE
Date: 14-12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2015
Publisher: arXiv
Date: 2014
Publisher: arXiv
Date: 2017
Publisher: arXiv
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Publisher: arXiv
Date: 2020
Publisher: arXiv
Date: 2014
Publisher: IEEE
Date: 12-2018
Publisher: Elsevier BV
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: arXiv
Date: 2012
Publisher: Elsevier BV
Date: 2020
Publisher: IEEE
Date: 09-2011
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Springer Science and Business Media LLC
Date: 07-12-2020
DOI: 10.1038/S41598-020-78323-0
Abstract: Local differential privacy has become the gold-standard of privacy literature for gathering or releasing sensitive in idual data points in a privacy-preserving manner. However, locally differential data can twist the probability density of the data because of the additive noise used to ensure privacy. In fact, the density of privacy-preserving data (no matter how many s les we gather) is always flatter in comparison with the density function of the original data points due to convolution with privacy-preserving noise density function. The effect is especially more pronounced when using slow-decaying privacy-preserving noises, such as the Laplace noise. This can result in under/over-estimation of the heavy-hitters. This is an important challenge facing social scientists due to the use of differential privacy in the 2020 Census in the United States. In this paper, we develop density estimation methods using smoothing kernels. We use the framework of deconvoluting kernel density estimators to remove the effect of privacy-preserving noise. This approach also allows us to adapt the results from non-parametric regression with errors-in-variables to develop regression models based on locally differentially private data. We demonstrate the performance of the developed methods on financial and demographic datasets.
Publisher: arXiv
Date: 2016
Publisher: CSIRO
Date: 2019
Publisher: IEEE
Date: 05-2020
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 07-2007
Publisher: arXiv
Date: 2018
Publisher: Elsevier BV
Date: 08-2015
Publisher: IEEE
Date: 12-2013
Publisher: Elsevier BV
Date: 02-2013
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 11-04-2021
Publisher: arXiv
Date: 2017
Publisher: arXiv
Date: 2021
Publisher: arXiv
Date: 2013
Publisher: arXiv
Date: 2013
Publisher: arXiv
Date: 2015
Publisher: arXiv
Date: 2015
Publisher: arXiv
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
Publisher: arXiv
Date: 2019
Publisher: Elsevier BV
Date: 2019
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2015
Publisher: Elsevier BV
Date: 04-2016
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 12-2012
Publisher: arXiv
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: arXiv
Date: 2012
Publisher: Elsevier BV
Date: 04-2020
Publisher: arXiv
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: Springer Singapore
Date: 22-11-2020
Publisher: Springer Science and Business Media LLC
Date: 10-06-2021
Publisher: IEEE
Date: 12-2018
Publisher: arXiv
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: Elsevier BV
Date: 2020
Publisher: Institution of Engineering and Technology (IET)
Date: 20-01-2011
Publisher: Institution of Engineering and Technology (IET)
Date: 13-10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: Springer Science and Business Media LLC
Date: 25-05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: arXiv
Date: 2020
Publisher: IEEE
Date: 12-2020
Publisher: arXiv
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
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: Informa UK Limited
Date: 31-08-2020
Publisher: arXiv
Date: 2021
Publisher: IEEE
Date: 06-2018
Publisher: arXiv
Date: 2020
Publisher: IEEE
Date: 12-2016
Publisher: arXiv
Date: 2020
Publisher: IEEE
Date: 12-2016
Publisher: Society for Industrial and Applied Mathematics
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: arXiv
Date: 2020
Publisher: arXiv
Date: 2019
Publisher: Springer Singapore
Date: 22-11-2020
Publisher: Elsevier BV
Date: 2014
Publisher: Elsevier BV
Date: 09-2007
DOI: 10.1016/J.AJEM.2007.01.007
Abstract: We present a 61-year-old patient who showed deep T wave inversion on her electrocardiogram (ECG) after cardioversion of her atrial flutter to sinus rhythm. A cardiac catheterization showed normal coronary arteries. The T wave inversion on her ECG is thought to be due to a cardiac memory phenomenon. Cardiac memory is a phenomenon that appears with T wave inversion on ECG after a change in the activation sequence of the heart. It may mimic cardiac ischemia and may mask any condition that appears with T wave abnormality on the ECG.
Publisher: IEEE
Date: 09-2015
Publisher: Elsevier BV
Date: 2013
Publisher: Springer Singapore
Date: 2019
Publisher: arXiv
Date: 2018
Publisher: IEEE
Date: 12-2016
Publisher: Informa UK Limited
Date: 22-02-2021
Publisher: Elsevier BV
Date: 2016
Publisher: IEEE
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: IEEE
Date: 04-2020
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 10-2013
Publisher: IEEE
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: IEEE
Date: 11-04-2021
Publisher: arXiv
Date: 2020
Publisher: IEEE
Date: 12-07-2021
Publisher: IEEE
Date: 06-2013
Publisher: arXiv
Date: 2018
Publisher: IEEE
Date: 12-2016
Start Date: 12-2021
End Date: 12-2024
Amount: $405,000.00
Funder: Australian Research Council
View Funded Activity