Privacy Guarantees and Risk Identification: Statistical Framework and Methodology

A risk-based approach to anonymization includes an assessment of the risk that an attack to reveal or uncover personal information will be realized, known as threat modelling, against the risk that an attack on the data will be successful (e.g., a re-identification). We wish to incorporate the provable guarantees of differential privacy into this assessment of risk, to produce safe data in context of the environment in which it will be used. We also need adapt the methods of statistical disclosure control to such an updated approach.

Opioid Use in Pediatric-Onset Inflammatory Bowel Disease - Year two

Patients with inflammatory bowel disease (IBD) have an inflamed digestive tract and experience diarrhea, fatigue, and abdominal pain. Youth with IBD are six times more likely to take opioids than youth without IBD. We are currently in the midst of an opioid crisis. In 2016, there were almost 3000 deaths related to opioid use in Canada. This increased to nearly 4000 deaths in 2017. Since 2001, opioid-related deaths have increased by 345% in the United States. IBD patients taking opioids have a poorer quality of life, regardless of how severe their IBD is.

Effects of low-dose radiation on immune parameters, antioxidant and metabolic signalling and implications in the development and progression of mammary cancer - Year two

Recent studies have called into question the Linear No Threshold (LNT) model of radiation protection, which predicts a linear increase in cancer risk with low-dose radiation exposure. However, current experimental evidence suggests that low-dose radiation (LDR,

An Automatic Tool for Developing Transactive Energy Smart-Contracts: Development, Validation and Integration with the IEMS Blockchain Platform

Energy consumers and prosumers are currently dealing with each other via utility companies, which is a slow, costly and indirect mechanism. With the aim of moving toward a free market, the goal of this project is to provide a suitable platform for automatic development and evolution of smart contracts in distributed transactive energy markets. This platform will make the blockchain technology, underlying smart contracts, applicable to direct transactions between energy consumers and prosumers, enabling additional steps towards a free market.

Safety Labs User and Performance Validation

The Safety Labs platform is an ‘always connected’ Internet of Things (IOT) cloud computing based system that focused on aging in place and well being for older adults through ongoing health knowledge.

Automatic Classification of Security Events

IBM QRadar needs to be able to categorize events generated by hundreds of different network devices in order to function as a Security Information and Event Management (SIEM). This categorization is currently a manual process and our aim is to automate this task. We have a database of over 579,000 events coming from over 300 devices that have been manually classified over the years. We also have the classification categories: 18 high level categories, broken down into 500+ subcategories; these categories broadly correspond to security threats.

Assessment of impacts of upstream developments and climate change on Carp River Watershed

There are plans for residential/commercial/industrial developments in upstream sections of Carp River Watershed (CRW). This will have impacts on the quantity and quality of the river water downstream as well as the sediment loads. In addition, due to climate change it is expected that both quantity and quality of the Carp River will deviate from the norms. Mississippi Valley Conservation Authority (MVCA) is in charge of managing and protecting Carp River Watershed.

Machine Learning for Network Management and Control

The goal of this research project is to identify ways to apply machine learning technology to help communication network operators cope with the vast amounts of data they must process to understand the health of their networks and to quickly resolve problems.

Embedded sensor fusion network

Highly accurate 3D object detectors require significant computational resources, and reducing computation and memory load while maintaining the same level of performance is a critical task for any safe and reliable autonomous vehicle. This research project investigates the deployment of an accurate 3D object detection model to a resource constrained architecture by changing the model structure, its parameters as well as its activity during operation.

AI-Based Automated Methodologies for Supply Chains: High Precision Tabular Detection and Semantic Modeling of Electronic Components from Datasheets

This project will develop a hybrid framework by integrating AI and machine learning methods with tabular information extraction and semantic modeling to improve the state-of-the-art precision and recall in tabular detection while maximizing the value of extracted information for industrial applications.

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