4 citations · 9 across the 8 of their papers we have counts for
9 papers
The Privacy-Hallucination Tradeoff in Differentially Private Language Models
Krithika Ramesh, Krishna Pillutla, Danish Pruthi +1
Both privacy and factual accuracy are paramount in high-stakes domains like healthcare. Concerningly, we uncover and investigate a privacy-hallucination tradeoff in differentially…
InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy
Vishnu Vinod, Krishna Pillutla, Abhradeep Guha Thakurta
As major progress in LLM-based long-form text generation enables paradigms such as retrieval-augmented generation (RAG) and inference-time scaling, safely incorporating private inf…
Correlated Noise Mechanisms for Differentially Private Learning
Krishna Pillutla, Jalaj Upadhyay, Christopher A. Choquette-Choo +9
This monograph explores the design and analysis of correlated noise mechanisms for differential privacy (DP), focusing on their application to private training of AI and machine le…
An Inversion Theorem for Buffered Linear Toeplitz (BLT) Matrices and Applications to Streaming Differential Privacy
H. Brendan McMahan, Krishna Pillutla
Buffered Linear Toeplitz (BLT) matrices are a family of parameterized lower-triangular matrices that play an important role in streaming differential privacy with correlated noise.…
Fine-Tuning Large Language Models with User-Level Differential Privacy
Zachary Charles, Arun Ganesh, Ryan McKenna +4
We investigate practical and scalable algorithms for training large language models (LLMs) with user-level differential privacy (DP) in order to provably safeguard all the examples…
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy
Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla +2
In the task of differentially private (DP) continual counting, we receive a stream of increments and our goal is to output an approximate running total of these increments, without…
Language Models can Subtly Deceive Without Lying: A Case Study on Strategic Phrasing in Legislation
Atharvan Dogra, Krishna Pillutla, Ameet Deshpande +5
We explore the ability of large language models (LLMs) to engage in subtle deception through strategically phrasing and intentionally manipulating information. This harmful behavio…
User Inference Attacks on Large Language Models
Nikhil Kandpal, Krishna Pillutla, Alina Oprea +3
Fine-tuning is a common and effective method for tailoring large language models (LLMs) to specialized tasks and applications. In this paper, we study the privacy implications of f…
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Krishna Pillutla +3
Differentially private learning algorithms inject noise into the learning process. While the most common private learning algorithm, DP-SGD, adds independent Gaussian noise in each…