97 citations · 214 across the 19 of their papers we have counts for
20 papers
Efficient DP-SGD for LLMs with Randomized Clipping
Enayat Ullah, Sai Aparna Aketi, Devansh Gupta +2
Large language models (LLMs) are trained on vast datasets that may contain sensitive information. Differential privacy (DP), the de facto standard for formal privacy guarantees, pr…
Federated Linear Contextual Bandits with User-level Differential Privacy
Ruiquan Huang, Huanyu Zhang, Luca Melis +3
This paper studies federated linear contextual bandits under the notion of user-level differential privacy (DP). We first introduce a unified federated bandits framework that can a…
Statler: State-Maintaining Language Models for Embodied Reasoning
Takuma Yoneda, Jiading Fang, Peng Li +7
There has been a significant research interest in employing large language models to empower intelligent robots with complex reasoning. Existing work focuses on harnessing their ab…
DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework
Hua Wang, Sheng Gao, Huanyu Zhang +2
Hyperparameter optimization, also known as hyperparameter tuning, is a widely recognized technique for improving model performance. Regrettably, when training private ML models, ma…
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Rachel Cummings, Damien Desfontaines, David Evans +21
In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's depl…
Contraction of Locally Differentially Private Mechanisms
Shahab Asoodeh, Huanyu Zhang
We investigate the contraction properties of locally differentially private mechanisms. More specifically, we derive tight upper bounds on the divergence between and outp…
Edgeworth Accountant: An Analytical Approach to Differential Privacy Composition
Hua Wang, Sheng Gao, Huanyu Zhang +3
In privacy-preserving data analysis, many procedures and algorithms are structured as compositions of multiple private building blocks. As such, an important question is how to eff…
Robust Estimation for Random Graphs
Jayadev Acharya, Ayush Jain, Gautam Kamath +2
We study the problem of robustly estimating the parameter of an Erdős-Rényi random graph on nodes, where a fraction of nodes may be adversarially corrupted. After showi…
Statistical Inference in the Differential Privacy Model
Huanyu Zhang
In modern settings of data analysis, we may be running our algorithms on datasets that are sensitive in nature. However, classical machine learning and statistical algorithms were…
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data
Gautam Kamath, Xingtu Liu, Huanyu Zhang
We study stochastic convex optimization with heavy-tailed data under the constraint of differential privacy (DP). Most prior work on this problem is restricted to the case where th…
Robust Testing and Estimation under Manipulation Attacks
Jayadev Acharya, Ziteng Sun, Huanyu Zhang
We study robust testing and estimation of discrete distributions in the strong contamination model. We consider both the "centralized setting" and the "distributed setting with inf…
Wide Network Learning with Differential Privacy
Huanyu Zhang, Ilya Mironov, Meisam Hejazinia
Despite intense interest and considerable effort, the current generation of neural networks suffers a significant loss of accuracy under most practically relevant privacy training…
Joint Design of Transmit Waveforms and Receive Filters for MIMO Radar via Manifold Optimization
Huanyu Zhang, Ziping Zhao
The problem of joint design of transmit waveforms and receive filters is desirable in many application scenarios of multiple-input multiple-output (MIMO) radar systems. In this pap…
Differentially Private Assouad, Fano, and Le Cam
Jayadev Acharya, Ziteng Sun, Huanyu Zhang
Le Cam's method, Fano's inequality, and Assouad's lemma are three widely used techniques to prove lower bounds for statistical estimation tasks. We propose their analogues under ce…
Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni +3
We initiate the study of hypothesis selection under local differential privacy. Given samples from an unknown probability distribution and a set of probability distribution…
Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni +1
We consider the problem of learning Markov Random Fields (including the prototypical example, the Ising model) under the constraint of differential privacy. Our learning goals incl…
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
Di Wang, Lijie Hu, Huanyu Zhang +2
In this paper, we study the problem of estimating smooth Generalized Linear Models (GLMs) in the Non-interactive Local Differential Privacy (NLDP) model. Different from its classic…
INSPECTRE: Privately Estimating the Unseen
Jayadev Acharya, Gautam Kamath, Ziteng Sun +1
We develop differentially private methods for estimating various distributional properties. Given a sample from a discrete distribution , some functional , and accuracy and p…
Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication
Jayadev Acharya, Ziteng Sun, Huanyu Zhang
We study the problem of estimating -ary distributions under -local differential privacy. samples are distributed across users who send privatized versions of th…
Differentially Private Testing of Identity and Closeness of Discrete Distributions
Jayadev Acharya, Ziteng Sun, Huanyu Zhang
We study the fundamental problems of identity testing (goodness of fit), and closeness testing (two sample test) of distributions over elements, under differential privacy. Whi…