3 citations · 3 across the 2 of their papers we have counts for
5 papers
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy
David Byrd, Vaikkunth Mugunthan, Antigoni Polychroniadou +1
Privacy-preserving federated learning enables a population of distributed clients to jointly learn a shared model while keeping client training data private, even from an untrusted…
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications
David Byrd, Antigoni Polychroniadou
Federated Learning enables a population of clients, working with a trusted server, to collaboratively learn a shared machine learning model while keeping each client's data within…
CryptoCredit: Securely Training Fair Models
Leo de Castro, Jiahao Chen, Antigoni Polychroniadou
When developing models for regulated decision making, sensitive features like age, race and gender cannot be used and must be obscured from model developers to prevent bias. Howeve…
Small Memory Robust Simulation of Client-Server Interactive Protocols over Oblivious Noisy Channels
T-H. Hubert Chan, Zhibin Liang, Antigoni Polychroniadou +1
We revisit the problem of low-memory robust simulation of interactive protocols over noisy channels. Haeupler [FOCS 2014] considered robust simulation of two-party interactive prot…
More is Less: Perfectly Secure Oblivious Algorithms in the Multi-Server Setting
T-H. Hubert Chan, Jonathan Katz, Kartik Nayak +2
The problem of Oblivious RAM (ORAM) has traditionally been studied in a single-server setting, but more recently the multi-server setting has also been considered. Yet it is still…