8 citations · 12 across the 6 of their papers we have counts for
6 papers
Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy
Tatsuki Koga, Ruihan Wu, Zhiyuan Zhang +1
With the recent remarkable advancement of large language models (LLMs), there has been a growing interest in utilizing them in the domains with highly sensitive data that lies outs…
Measuring Privacy Loss in Distributed Spatio-Temporal Data
Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri
Statistics about traffic flow and people's movement gathered from multiple geographical locations in a distributed manner are the driving force powering many applications, such as…
Differentially Private Multi-Site Treatment Effect Estimation
Tatsuki Koga, Kamalika Chaudhuri, David Page
Patient privacy is a major barrier to healthcare AI. For confidentiality reasons, most patient data remains in silo in separate hospitals, preventing the design of data-driven heal…
Population Expansion for Training Language Models with Private Federated Learning
Tatsuki Koga, Congzheng Song, Martin Pelikan +1
Federated learning (FL) combined with differential privacy (DP) offers machine learning (ML) training with distributed devices and with a formal privacy guarantee. With a large pop…
Privacy Amplification by Subsampling in Time Domain
Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri
Aggregate time-series data like traffic flow and site occupancy repeatedly sample statistics from a population across time. Such data can be profoundly useful for understanding tre…
General-to-Detailed GAN for Infrequent Class Medical Images
Tatsuki Koga, Naoki Nonaka, Jun Sakuma +1
Deep learning has significant potential for medical imaging. However, since the incident rate of each disease varies widely, the frequency of classes in a medical image dataset is…