31 citations · 44 across the 11 of their papers we have counts for
4 papers · 1 filter
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging
Naif Alkhunaizi, Dmitry Kamzolov, Martin Takáč +1
Collaboration among multiple data-owning entities (e.g., hospitals) can accelerate the training process and yield better machine learning models due to the availability and diversi…
Efficient Encrypted Inference on Ensembles of Decision Trees
Kanthi Sarpatwar, Karthik Nandakumar, Nalini Ratha +4
Data privacy concerns often prevent the use of cloud-based machine learning services for sensitive personal data. While homomorphic encryption (HE) offers a potential solution by e…
Efficient CNN Building Blocks for Encrypted Data
Nayna Jain, Karthik Nandakumar, Nalini Ratha +2
Machine learning on encrypted data can address the concerns related to privacy and legality of sharing sensitive data with untrustworthy service providers. Fully Homomorphic Encryp…
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
Lingjuan Lyu, Yitong Li, Karthik Nandakumar +2
This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens a…