3 papers
cs.CR2026
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…
cs.LG2026
Differential Privacy Analysis of Decentralized Gossip Averaging under Varying Threat Models
Antti Koskela, Tejas Kulkarni
Achieving differential privacy (DP) guarantees in fully decentralized machine learning is challenging due to the absence of a central aggregator and varying trust assumptions among…
cs.LG2025
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…