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This is the repository for Project of COMP 530 Data Privacy and Security course given by Emre Gursoy at Koc University. Code is written by Esad Simitcioglu, Arman Torikoglu, and Alireza Khodaie
O objetivo deste projeto de iniciação científica é estudar a área de Privacy Preserving Machine Learning (PPML), que se dedica a encontrar soluções para realizar aprendizado de máquina de forma segura e preservando a privacidade dos dados.
Birhanu Eshete is an Associate Professor of Computer Science at the University of Michigan, Dearborn. His main research focus is in trustworthy machine learning with emphasis on security, safety, privacy, interpretability, fairness, and the dynamics thereof. He also studies online cybercrime and advanced and persistent threats (APTs).
FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.
This list provides up-to-date resources pertaining to the research and development of privacy-preserving deep learning, with many of them cited in the paper titled "A Comprehensive Survey and Taxonomy on Privacy-Preserving Deep Learning".