4 papers
Federated Differentially Private Optimization via Low-Rank Preconditioning
Antti Koskela, Tejas Kulkarni, Elias Raninen
We consider differentially private (DP) optimization in the federated setting, where a DP estimate of the feature covariance matrix serves as a Hessian approximation for generalize…
Membership Inference Attacks for Retrieval Based In-Context Learning for Document Question Answering
Tejas Kulkarni, Antti Koskela, Laith Zumot
We show that remotely hosted applications employing in-context learning when augmented with a retrieval function to select in-context examples can be vulnerable to membership-infer…
Differentially Private In-Context Learning with Nearest Neighbor Search
Antti Koskela, Tejas Kulkarni, Laith Zumot
Differentially private in-context learning (DP-ICL) has recently become an active research topic due to the inherent privacy risks of in-context learning. However, existing approac…
Differential Privacy of Decentralized Learning: Centralized Rate Conditions and Nearly Tight Guarantees
Antti Koskela, Tejas Kulkarni
We analyze the differential privacy of gossip averaging on a graph with independent Gaussian noise at every node, as seen by a subset of observing nodes. We characterize the privac…