University of Winnipeg
http://winnspace.uwinnipeg.ca:80/xmlui
The WinnSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.2024-03-19T11:09:59ZCryptographic Techniques for Data Privacy in Digital Forensics
https://hdl.handle.net/10680/2131
Cryptographic Techniques for Data Privacy in Digital Forensics
Ogunseyi, Taiwo Blessing; Oluwasola, Mary Adedayo
The acquisition and analysis of data in digital forensics raise different data privacy challenges. Many existing works on digital forensic readiness discuss what information should be stored and how to collect relevant data to facilitate investigations. However, the cost of this readiness often directly impacts the privacy of innocent third parties and suspects if the collected information is irrelevant. Approaches that have been suggested for privacy-preserving digital forensics focus on the use of policy, non-cryptography-based, and cryptography-based solutions. Cryptographic techniques have been proposed to address issues of data privacy during data analysis. As the utilization of some of these cryptographic techniques continues to increase, it is important to evaluate their applicability and challenges in relation to digital forensics processes. This study provides digital forensics investigators and researchers with a roadmap to understanding the data privacy challenges in digital forensics and examines the various privacy techniques that can be utilized to tackle these challenges. Specifically, we review the cryptographic techniques applied for privacy protection in digital forensics and categorize them within the context of whether they support trusted third parties, multiple investigators, and multi-keyword searches. We highlight some of the drawbacks of utilizing cryptography-based methods in privacy-preserving digital forensics and suggest potential solutions to the identified shortcomings. In addition, we propose a conceptual privacy-preserving digital forensics (PPDF) model that is based on the use of cryptographic techniques and analyze the model within the context of the above-mentioned factors. An evaluation of the model is provided through a consideration of identified factors that may affect an investigation. Lastly, we provide an analysis of how existing principles for preserving privacy in digital forensics are addressed in our PPDF model. Our evaluation shows that the model aligns with many of the existing privacy principles recommended for privacy protection in digital forensics.
2023-12-15T00:00:00ZResolving Stock Structure of Sauger (Sander canadensis) in Manitoba, Canada using Biometric, Isotopic, and Genetic Approaches
https://hdl.handle.net/10680/2129
Resolving Stock Structure of Sauger (Sander canadensis) in Manitoba, Canada using Biometric, Isotopic, and Genetic Approaches
Wong, Caleb H. S.
Many sauger (Sander canadensis) populations in Manitoba have declined in numbers and biomass. Fisheries managers have proposed a province-wide sauger management plan to protect and restore sauger populations, but they are uncertain how sauger populations should be defined and to what extent they may interact. In this thesis, I used a multifaceted approach to resolve population structure and identify migratory corridors of sauger in Manitoba. First, I mined biometric data from several long-term monitoring datasets to calculate life history indices for sauger stocks across 29 waterbodies. Sauger growth generally decreased and the age at 50% maturity increased among lakes of increasing latitude. This trend was also observed within Lake Winnipeg, yet the length at 50% maturity remained constant. Sauger grew exceptionally fast in Lake Manitoba and Lake Winnipegosis and matured at an early age. Next, I performed a stable isotope analysis (13C and 15N) of sauger tissue to investigate contemporary sauger migration throughout the Lake Winnipeg watershed. Sauger from Lake Winnipeg, Lake Manitoba, and Lac du Bonnet occupied distinct isotopic niches, and I identified several possible migrants from Lake Manitoba and the Winnipeg River in Lake Winnipeg. Finally, I used microsatellites to assess the genetic health and structure of sauger stocks across Manitoba. Genetic diversity within sample populations was moderate to high, and incidence of inbreeding and hybridization with walleye (Sander vitreus) was low. I identified four broad genetic sauger stocks: Lake Winnipeg; Lake Manitoba and Lake Winnipegosis; the Red and Assiniboine Rivers; and the Churchill and Saskatchewan Rivers. Gene flow between Lake Winnipeg and Lake Manitoba stocks is minimal. These findings will assist managers in defining stock management units and optimizing management efforts for sauger populations in Manitoba.
2023-12-06T00:00:00ZA Critical Analysis of Canada’s Sex Work Discourse and Policy: From Federal to Local
https://hdl.handle.net/10680/2128
A Critical Analysis of Canada’s Sex Work Discourse and Policy: From Federal to Local
Hickson, Dana
Through an analysis of the 2022 federal House of Commons standing committee review of the Protection of Communities and Exploited Persons Act and selected provincial and municipal documents, this thesis illustrates some of the ways that the discourses articulated during national policy debates empower certain groups while disempowering the people these legislative responses ostensibly support and protect. I turn to the Manitoba context to demonstrate how these discourses play out through a study of local policy and programming. Through a conjunctural analysis, this thesis argues that a massive number of resources are being poured into police, prosecution services, and community organizations to combat sex work in Manitoba.
2023-12-18T00:00:00ZExploring Hyperspectral Imaging and 3D Convolutional Neural Network for Stress Classification in Plants
https://hdl.handle.net/10680/2127
Exploring Hyperspectral Imaging and 3D Convolutional Neural Network for Stress Classification in Plants
Noshiri, Nooshin
Hyperspectral imaging (HSI) has emerged as a transformative technology in imaging, characterized by its ability to capture a wide spectrum of light, including wavelengths beyond the visible range. This approach significantly differs from traditional imaging methods such as RGB imaging, which uses three color channels, and multispectral imaging, which captures several discrete spectral bands. Through this approach, HSI offers detailed spectral signatures for each pixel, facilitating a more nuanced analysis of the imaged subjects. This capability is particularly beneficial in applications like agricultural practices, where it can detect changes in physiological and structural characteristics of crops. Moreover, the ability of HSI to monitor these changes over time is advantageous for observing how subjects respond to different environmental conditions or treatments. However, the high-dimensional nature of hyperspectral data presents challenges in data processing and feature extraction. Traditional machine learning algorithms often struggle to handle such complexity. This is where 3D Convolutional Neural Networks (CNNs) become valuable. Unlike 1D-CNNs, which extract features from spectral dimensions, and 2D-CNNs, which focus on spatial dimensions, 3D CNNs have the capability to process data across both spectral and spatial dimensions. This makes them adept at extracting complex features from hyperspectral data. In this thesis, we explored the potency of HSI combined with 3D-CNN in agriculture domain where plant health and vitality are paramount. To evaluate this, we subjected lettuce plants to varying stress levels to assess the performance of this method in classifying the stressed lettuce at the early stages of growth into their respective stress-level groups. For this study, we created a dataset comprising 88 hyperspectral image samples of stressed lettuce. Utilizing Bayesian optimization, we developed 350 distinct 3D-CNN models to assess the method. The top-performing model achieved a 75.00\% test accuracy. Additionally, we addressed the challenge of generating valid 3D-CNN models in the Keras Tuner library through meticulous hyperparameter configuration. Our investigation also extends to the role of individual channels and channel groups within the color and near-infrared spectrum in predicting results for each stress-level group. We observed that the red and green spectra have a higher influence on the prediction results. Furthermore, we conducted a comprehensive review of 3D-CNN-based classification techniques for diseased and defective crops using non-UAV-based hyperspectral images.
2023-12-06T00:00:00Z