Facebook Report on Privacy of fNIRS data

Autor:	Hossen, Md Imran, Chilukoti, Sai Venkatesh, Shan, Liqun, Tida, Vijay Srinivas, Hei, Xiali
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security I.2.0
Druh dokumentu:	Working Paper
Popis:	The primary goal of this project is to develop privacy-preserving machine learning model training techniques for fNIRS data. This project will build a local model in a centralized setting with both differential privacy (DP) and certified robustness. It will also explore collaborative federated learning to train a shared model between multiple clients without sharing local fNIRS datasets. To prevent unintentional private information leakage of such clients' private datasets, we will also implement DP in the federated learning setting. Comment: 15 pages, 5 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.00973 Zobrazit plný text záznamu View this record from Arxiv