The goal of the project is to secure authentication of a template, especially a biometric query, without compromising the template, the database, or the query; in case of database attack or a corrupted communication channel.
This project explores domain adaptation & online learning for model customization considering privacy preservation and secure communication. We propose blockchain-based peer-to-peer federated learning (P2PDL) using federated learning applications.
The goal of the project is to secure authentication of a template, especially a biometric query, without compromising the template, the database, or the query; in case of database attack or a corrupted communication channel.
The goal of this project is to develop a Deep Neural Network based risk management model that can help financial companies predict loan default likelihood with a higher accuracy when a customer applies for a loan.
The goal of this project is to explore potential vulnerabilities in federated learning applications. Federated learning is a new kind of distributed machine learning with decentralized data. There is no need for data sharing for federated learning.