Publications by authors named "Aneeqa Ijaz"

2 Publications

  • Page 1 of 1

Communication Requirements in 5G-Enabled Healthcare Applications: Review and Considerations.

Healthcare (Basel) 2022 Feb 2;10(2). Epub 2022 Feb 2.

Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.

Fifth generation (5G) mobile communication technology can enable novel healthcare applications and augment existing ones. However, 5G-enabled healthcare applications demand diverse technical requirements for radio communication. Knowledge of these requirements is important for developers, network providers, and regulatory authorities in the healthcare sector to facilitate safe and effective healthcare. In this paper, we review, identify, describe, and compare the requirements for communication key performance indicators in relevant healthcare use cases, including remote robotic-assisted surgery, connected ambulance, wearable and implantable devices, and service robotics for assisted living, with a focus on quantitative requirements. We also compare 5G-healthcare requirements with the current state of 5G capabilities. Finally, we identify gaps in the existing literature and highlight considerations for this space.
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http://dx.doi.org/10.3390/healthcare10020293DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8872156PMC
February 2022

Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security.

Front Big Data 2020 12;3:587139. Epub 2020 Nov 12.

Hamad Bin Khalifa University (HBKU), Doha, Qatar.

With the advances in machine learning (ML) and deep learning (DL) techniques, and the potency of cloud computing in offering services efficiently and cost-effectively, Machine Learning as a Service (MLaaS) cloud platforms have become popular. In addition, there is increasing adoption of third-party cloud services for outsourcing training of DL models, which requires substantial costly computational resources (e.g., high-performance graphics processing units (GPUs)). Such widespread usage of cloud-hosted ML/DL services opens a wide range of attack surfaces for adversaries to exploit the ML/DL system to achieve malicious goals. In this article, we conduct a systematic evaluation of literature of cloud-hosted ML/DL models along both the important dimensions- and -related to their security. Our systematic review identified a total of 31 related articles out of which 19 focused on attack, six focused on defense, and six focused on both attack and defense. Our evaluation reveals that there is an increasing interest from the research community on the perspective of attacking and defending different attacks on Machine Learning as a Service platforms. In addition, we identify the limitations and pitfalls of the analyzed articles and highlight open research issues that require further investigation.
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http://dx.doi.org/10.3389/fdata.2020.587139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931962PMC
November 2020
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