Cover Image

ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Peihan, Rosenblatt, Lucas, Kong, Weiwei, Ponomareva, Natalia, Kamath, Gautam, Cummings, Rachel, Geambasu, Roxana, Gan, Yu, Tsai, Lillian, Bie, Alex
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Computation and Language Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data
by: Rosenblatt, Lucas, et al.
Published: (2026)

Clustering and Median Aggregation Improve Differentially Private Inference
by: Amin, Kareem, et al.
Published: (2025)

Are Data Experts Buying into Differentially Private Synthetic Data? Gathering Community Perspectives
by: Rosenblatt, Lucas, et al.
Published: (2024)

Private prediction for large-scale synthetic text generation
by: Amin, Kareem, et al.
Published: (2024)

Engineering Robustness into Personal Agents with the AI Workflow Store
by: Geambasu, Roxana, et al.
Published: (2026)

Optimal Differentially Private Sampling of Unbounded Gaussians
by: Iverson, Valentio, et al.
Published: (2025)

Differential Privacy Under Class Imbalance: Methods and Empirical Insights
by: Rosenblatt, Lucas, et al.
Published: (2024)

Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
by: Tramèr, Florian, et al.
Published: (2022)

Decomposing Private Image Generation via Coarse-to-Fine Wavelet Modeling
by: Bayrooti, Jasmine, et al.
Published: (2026)

On the Learnability of Distribution Classes with Adaptive Adversaries
by: Lechner, Tosca, et al.
Published: (2025)

Differentially Private Post-Processing for Fair Regression
by: Xian, Ruicheng, et al.
Published: (2024)

Thompson Sampling Itself is Differentially Private
by: Ou, Tingting, et al.
Published: (2024)

Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models
by: Carranza, Aldo Gael, et al.
Published: (2023)

Rethinking Benchmarks for Differentially Private Image Classification
by: Mokhtari, Sabrina, et al.
Published: (2026)

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
by: Ponomareva, Natalia, et al.
Published: (2025)

Cookie Monster: Efficient On-device Budgeting for Differentially-Private Ad-Measurement Systems
by: Tholoniat, Pierre, et al.
Published: (2024)

Private Mean Estimation with Person-Level Differential Privacy
by: Agarwal, Sushant, et al.
Published: (2024)

Learning from Synthetic Data: Limitations of ERM
by: Amin, Kareem, et al.
Published: (2026)

Distribution Learnability and Robustness
by: Ben-David, Shai, et al.
Published: (2024)

Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
by: Yu, Da, et al.
Published: (2022)

An Optimization Framework for Differentially Private Sparse Fine-Tuning
by: Makni, Mehdi, et al.
Published: (2025)

Differentially Private Optimization for Non-Decomposable Objective Functions
by: Kong, Weiwei, et al.
Published: (2023)

The Knowability Argument and the Syntactic Type-Theoretic Approach
by: Lucas Rosenblatt
Published: (2014)

Symposium on Uncut: Introduction
by: Lucas Rosenblatt
Published: (2021)

The Broader Landscape of Robustness in Algorithmic Statistics
by: Kamath, Gautam
Published: (2024)

Not All Learnable Distribution Classes are Privately Learnable
by: Bun, Mark, et al.
Published: (2024)

SynBench: A Benchmark for Differentially Private Text Generation
by: Sun, Yidan, et al.
Published: (2025)

Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance
by: Gu, Xin, et al.
Published: (2023)

CoinPress: Practical Private Mean and Covariance Estimation
by: Biswas, Sourav, et al.
Published: (2020)

Integrating Differential Privacy and Contextual Integrity
by: Benthall, Sebastian, et al.
Published: (2024)

The Discrete Gaussian for Differential Privacy
by: Canonne, Clément L., et al.
Published: (2020)

Differentially Private Knowledge Distillation via Synthetic Text Generation
by: Flemings, James, et al.
Published: (2024)

Secret-Protected Evolution for Differentially Private Synthetic Text Generation
by: Wang, Tianze, et al.
Published: (2025)

Struct-Bench: A Benchmark for Differentially Private Structured Text Generation
by: Wang, Shuaiqi, et al.
Published: (2025)

Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
by: Huang, Alyssa, et al.
Published: (2023)

Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model
by: Cummings, Rachel, et al.
Published: (2025)

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
by: Hod, Shlomi, et al.
Published: (2025)

Centering Policy and Practice: Research Gaps around Usable Differential Privacy
by: Cummings, Rachel, et al.
Published: (2024)

Assignment Algorithms for Multi-Robot Multi-Target Tracking with Sufficient and Limited Sensing Capability
by: Li, Peihan, et al.
Published: (2023)

Differentially Private Optimization with Sparse Gradients
by: Ghazi, Badih, et al.
Published: (2024)