Cover Image

Near-Optimal differentially private low-rank trace regression with guaranteed private initialization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Zha, Mengyue
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Modulated learning for private and distributed regression with just a single sample per client device
by: Vepakomma, Praneeth, et al.
Published: (2026)

Differentially private Bayesian tests
by: Chakraborty, Abhisek, et al.
Published: (2024)

The importance of feature preprocessing for differentially private linear optimization
by: Sun, Ziteng, et al.
Published: (2023)

Randomized algorithms for precise measurement of differentially-private, personalized recommendations
by: Laro, Allegra, et al.
Published: (2023)

DNA: Differentially private Neural Augmentation for contact tracing
by: Romijnders, Rob, et al.
Published: (2024)

Almost linear time differentially private release of synthetic graphs
by: Liu, Jingcheng, et al.
Published: (2024)

Resampling methods for private statistical inference
by: Chadha, Karan, et al.
Published: (2024)

Harnessing large-language models to generate private synthetic text
by: Kurakin, Alexey, et al.
Published: (2023)

Accurate, private, secure, federated U-statistics with higher degree
by: Sinh, Quentin, et al.
Published: (2026)

Uncertainty quantification by block bootstrap for differentially private stochastic gradient descent
by: Dette, Holger, et al.
Published: (2024)

Data value estimation on private gradients
by: Zhou, Zijian, et al.
Published: (2024)

Differentially private and decentralized randomized power method
by: Nicolas, Julien, et al.
Published: (2024)

Training generative models from privatized data
by: Reshetova, Daria, et al.
Published: (2023)

Nonparametric extensions of randomized response for private confidence sets
by: Waudby-Smith, Ian, et al.
Published: (2022)

Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training
by: Usynin, Dmitrii, et al.
Published: (2023)

Integrating uncertainty quantification into randomized smoothing based robustness guarantees
by: Däubener, Sina, et al.
Published: (2024)

Differentially private projection-depth-based medians
by: Ramsay, Kelly, et al.
Published: (2023)

The 2020 US Decennial Census is more private than you (might) think
by: Su, Buxin, et al.
Published: (2024)

DKiS: Decay weight invertible image steganography with private key
by: Yang, Hang, et al.
Published: (2023)

Efficient derandomization of differentially private counting queries
by: Ghentiyala, Surendra
Published: (2025)

An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees
by: Tran, Hoang, et al.
Published: (2026)

Can LLMs get help from other LLMs without revealing private information?
by: Hartmann, Florian, et al.
Published: (2024)

An Efficient and Multi-private Key Secure Aggregation for Federated Learning
by: Yang, Xue, et al.
Published: (2023)

Near-Optimal Reinforcement Learning with Shuffle Differential Privacy
by: Bai, Shaojie, et al.
Published: (2024)

Tangent differential privacy
by: Ying, Lexing
Published: (2024)

Metadata-private Messaging without Coordination
by: Jiang, Peipei, et al.
Published: (2025)

LRD-MPC: Efficient MPC Inference through Low-rank Decomposition
by: Tang, Tingting, et al.
Published: (2026)

Pre-training Differentially Private Models with Limited Public Data
by: Bu, Zhiqi, et al.
Published: (2024)

How reparametrization trick broke differentially-private text representation learning
by: Habernal, Ivan
Published: (2022)

An empirical analysis of zero-day vulnerabilities disclosed by the zero day initiative
by: Shet, Apurva, et al.
Published: (2025)

Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
by: Lowy, Andrew, et al.
Published: (2025)

Differentially private multivariate medians
by: Ramsay, Kelly, et al.
Published: (2022)

Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)

Bistochastically private release of longitudinal data
by: Ruiz, Nicolas
Published: (2025)

Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
by: Liu, Xiao-Yang, et al.
Published: (2023)

Bistochastically private release of data streams with zero delay
by: Ruiz, Nicolas
Published: (2024)

Practical Bayes-Optimal Membership Inference Attacks
by: Lassila, Marcus, et al.
Published: (2025)

Optimal Fairness under Local Differential Privacy
by: Ghoukasian, Hrad, et al.
Published: (2025)

Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor
by: Aliakbarpour, Maryam, et al.
Published: (2025)

Non-omniscient backdoor injection with one poison sample: Proving the one-poison hypothesis for linear regression, linear classification, and 2-layer ReLU neural networks
by: Peinemann, Thorsten, et al.
Published: (2025)