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Olayinka Adeboye
Also published under:
Affiliation
School of Science, Engineering, and Environment University of Salford, Manchester, United Kingdom
Topic
Autonomous Vehicles,Camera Data,Distortion,Feature Matching,Generative Adversarial Networks,Geolocated,Image Registration,Imaging Data,Inference Attacks,Street View,Street View Images,Balanced Trade-off,Clustering Objective,Compact Set,Conditional Variational Autoencoder,Data Generation,Data Storage,Deep Learning,Differential Privacy,Distinct Features,Distinct Selection,Encoder Network,Feature Information,Gaussian Mixture Model,Global Features,Image Features,Image Object,Image Pyramid,Local Features,Localization Accuracy,Location Privacy,Matching Reference,Mixture Model,Noisy Images,Pairwise Clustering,Privacy Preservation,Privacy Protection,Query Data,Query Features,Query Image,Reference Data,Reference Image,Robust Deep Learning,Scale Space,Synthetic Images,Types Of Attacks,Utility Of Imaging,Variational Autoencoder,
Biography
Olayinka Adeboye received the M.Sc. degree in cybersecurity and threat intelligence with merits and a dissertation on the critical security and privacy concerns on social networks.
He is currently a Research Student with the University of Salford, with a focus on research interests in cybersecurity, data privacy, data analytics, and artificial intelligence. His Ph.D. research focuses on the privacy-preservation of connected and autonomous vehicle data, intending to analyze the privacy risks associated with sensitive data and highlight potential threats to the system. As a solution to efficient camera data privacy-preservation for autonomous vehicle data storage and processing, he developed a private generative technique to address a balanced privacy-utility tradeoff. He delivered talks at the Salford postgraduate research conferences about his work and was awarded the best presentation. He also engages as a teaching assistant to support his supervisors in delivering workshops and hands-on experiments for level five and seven students.
He is currently a Research Student with the University of Salford, with a focus on research interests in cybersecurity, data privacy, data analytics, and artificial intelligence. His Ph.D. research focuses on the privacy-preservation of connected and autonomous vehicle data, intending to analyze the privacy risks associated with sensitive data and highlight potential threats to the system. As a solution to efficient camera data privacy-preservation for autonomous vehicle data storage and processing, he developed a private generative technique to address a balanced privacy-utility tradeoff. He delivered talks at the Salford postgraduate research conferences about his work and was awarded the best presentation. He also engages as a teaching assistant to support his supervisors in delivering workshops and hands-on experiments for level five and seven students.