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Title: Differential privacy in machine learning
Authors: Tan, Nicole
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Tan, N. (2022). Differential privacy in machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE21-0019
Abstract: With a surge in the use of machine learning, stakeholders have no visibility into the activities of processes that were run on their private data. When it comes to sharing data to train these machine learning models, there is a rising concern about privacy. Federated learning was introduced as a type of distributed machine learning. Stakeholders will keep their data local in a federated learning approach. This alone is not enough to protect the privacy of stakeholders’ data. Attacks targeting the parameters used to train models have increased as a result of the increased usage of a federated learning approach to train models, and these attacks may possibly provide attackers access to confidential data. The objective of this project is to use federated learning to create a shared model architecture that incorporates differential privacy on various neural network architectures.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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