Enabling Deep Learning in a Resource-Limited Environment with Data Privacy and Software Security
Enabling Deep Learning in a Resource-Limited Environment with Data Privacy and Software Security
In today’s data-driven world, deep learning (DL) has been widely used in various applications, including precision health/medicine, computer vision, and natural language processing, due to its superior performance. Moreover, DL usually requires huge data and computational resources for its training. However, data can be sensitive, like health data, so its privacy is always a concern, and data source does not always have sufficient resources to run DL. In this regard, we have two critical questions (1) how to preserve data privacy in DL to increase the collaboration among data sources? and (2) how to enable DL in a resource-constrained environment? This talk will present the answers to these questions in the first part of this presentation.
The second part will be in a different area. It will focus on software security. More specifically, it will present how to leverage transformer-based language models (which outperform other models in natural language processing) in software vulnerability detection.
About the speaker
Research Scientist at CSIRO Data61
Chandra Thapa received his Ph.D. degree from the University of Newcastle, Australia, in 2018. His current works are in the field of privacy-preserving machine learning, machine learning on resource limited environment, application of machine learning to security, distributed systems security, and network information theory. Chandra is currently with CSIRO’s Data61 as a research scientist.
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