Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140365
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dc.contributor.authorLim, Royce Jin Fengen_US
dc.date.accessioned2020-05-28T05:39:48Z-
dc.date.available2020-05-28T05:39:48Z-
dc.date.issued2020-
dc.identifier.urihttps://hdl.handle.net/10356/140365-
dc.description.abstractThe objective of this project is to learn latent representations using a Machine Learning approach for image sanitization in Smart Homes surveillance cameras. Three Autoencoder Machine Learning models are explored in this project 1) Adversarial Autoencoder 2) Variational Autoencoder 3) Variational Fair Autoencoder. These autoencoders are able to learn latent representations of the input data, which can be processed to encourage separation between the input data and private information which are classified in the model as sensitive variables. This provides the capability of sanitizing the image of the Smart Home user’s private information.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationA3250-191en_US
dc.subjectEngineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleMachine learning based privacy mechanismsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorTay, Wee Pengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailwptay@ntu.edu.sgen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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