Imagen de Portada

Challenges in Enabling Private Data Valuation

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Fu, Yiwei, Wang, Tianhao, Chandrasekaran, Varun
Formato:	Preprint
Publicado:	2026
Materias:	Cryptography and Security Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Data Valuation and Detections in Federated Learning
por: Li, Wenqian, et al.
Publicado: (2023)

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
por: Gao, Fengyu, et al.
Publicado: (2024)

The Erasure Illusion: Stress-Testing the Generalization of LLM Forgetting Evaluation
por: Jia, Hengrui, et al.
Publicado: (2025)

Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
por: Zheng, Shuyuan, et al.
Publicado: (2025)

Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private
por: Wu, Ruihan, et al.
Publicado: (2025)

Reconstruction of Differentially Private Text Sanitization via Large Language Models
por: Pang, Shuchao, et al.
Publicado: (2024)

Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis
por: Kotal, Anantaa, et al.
Publicado: (2023)

Differentially Private Synthetic Data Release for Topics API Outputs
por: Dick, Travis, et al.
Publicado: (2025)

AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMs
por: Krishna, Varun Badrinath
Publicado: (2024)

Differentially Private Preference Data Synthesis for Large Language Model Alignment
por: Gao, Fengyu, et al.
Publicado: (2026)

Differentially Private Synthetic Data Generation Using Context-Aware GANs
por: Kotal, Anantaa, et al.
Publicado: (2025)

DP-TabICL: In-Context Learning with Differentially Private Tabular Data
por: Carey, Alycia N., et al.
Publicado: (2024)

Differentially Private Publication of Electricity Time Series Data in Smart Grids
por: Shaham, Sina, et al.
Publicado: (2024)

dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
por: Mahiou, Sofiane, et al.
Publicado: (2025)

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
por: Li, Yuecheng, et al.
Publicado: (2024)

Differentially Private Model Merging
por: Yin, Qichuan, et al.
Publicado: (2026)

Privately Aligning Language Models with Reinforcement Learning
por: Wu, Fan, et al.
Publicado: (2023)

Systematic Scaling Analysis of Jailbreak Attacks in Large Language Models
por: Wang, Xiangwen, et al.
Publicado: (2026)

EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors
por: Banayeeanzade, Amin, et al.
Publicado: (2026)

Differentially Private Worst-group Risk Minimization
por: Zhou, Xinyu, et al.
Publicado: (2024)

Provable Privacy with Non-Private Pre-Processing
por: Hu, Yaxi, et al.
Publicado: (2024)

Improved Algorithms for Differentially Private Language Model Alignment
por: Chen, Keyu, et al.
Publicado: (2025)

Linearizing Models for Efficient yet Robust Private Inference
por: Sarkar, Sreetama, et al.
Publicado: (2024)

Differentially Private Iterative Screening Rules for Linear Regression
por: Khanna, Amol, et al.
Publicado: (2025)

Differentially Private Deep Model-Based Reinforcement Learning
por: Rio, Alexandre, et al.
Publicado: (2024)

Provable Differentially Private Computation of the Cross-Attention Mechanism
por: Ke, Yekun, et al.
Publicado: (2024)

Differentially Private In-Context Learning with Nearest Neighbor Search
por: Koskela, Antti, et al.
Publicado: (2025)

Evaluating Differentially Private Generation of Domain-Specific Text
por: Sun, Yidan, et al.
Publicado: (2025)

Taming Data Challenges in ML-based Security Tasks Using Generative AI
por: Kanchi, Shravya, et al.
Publicado: (2025)

Differentially Private Block-wise Gradient Shuffle for Deep Learning
por: Zagardo, David
Publicado: (2024)

Privacy Assessment of Federated Learning using Private Personalized Layers
por: Jourdan, Théo, et al.
Publicado: (2021)

Deep Efficient Private Neighbor Generation for Subgraph Federated Learning
por: Zhang, Ke, et al.
Publicado: (2024)

Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
por: Piran, Fardin Jalil, et al.
Publicado: (2024)

FERRET: Private Deep Learning Faster And Better Than DPSGD
por: Zagardo, David
Publicado: (2025)

KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data
por: Zhou, Andy, et al.
Publicado: (2024)

Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists
por: Ly, Timothée, et al.
Publicado: (2024)

Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference
por: Xu, Xiangrui, et al.
Publicado: (2024)

A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization
por: Panda, Ashwinee, et al.
Publicado: (2022)

Private Prediction Sets
por: Angelopoulos, Anastasios N., et al.
Publicado: (2021)

Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning
por: Fu, Xiaowen, et al.
Publicado: (2024)