Cover Image

Mean estimation in the add-remove model of differential privacy

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kulesza, Alex, Suresh, Ananda Theertha, Wang, Yuyan
Format:	Preprint
Published:	2023
Subjects:	Data Structures and Algorithms Cryptography and Security Information Theory Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
by: Asi, Hilal, et al.
Published: (2024)

Subset-Based Instance Optimality in Private Estimation
by: Dick, Travis, et al.
Published: (2023)

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

Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples
by: Afzali, Mohammad, et al.
Published: (2023)

Robustness Implies Privacy in Statistical Estimation
by: Hopkins, Samuel B., et al.
Published: (2022)

Reveal-or-Obscure: A Differentially Private Sampling Algorithm for Discrete Distributions
by: Tasnim, Naima, et al.
Published: (2025)

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

Online Distribution Learning with Local Private Constraints
by: Sima, Jin, et al.
Published: (2024)

Analysis of Shuffling Beyond Pure Local Differential Privacy
by: Takagi, Shun, et al.
Published: (2026)

Agnostic Private Density Estimation for GMMs via List Global Stability
by: Afzali, Mohammad, et al.
Published: (2024)

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

Better and Simpler Lower Bounds for Differentially Private Statistical Estimation
by: Narayanan, Shyam
Published: (2023)

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

Optimal Rates for Differentially Private Hypothesis Testing with E-values
by: Jacobsen, Ben, et al.
Published: (2026)

PLAN: Variance-Aware Private Mean Estimation
by: Aumüller, Martin, et al.
Published: (2023)

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

SpecTr: Fast Speculative Decoding via Optimal Transport
by: Sun, Ziteng, et al.
Published: (2023)

Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters
by: Haney, Samuel, et al.
Published: (2023)

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach
by: Jiang, Bo, et al.
Published: (2024)

Compression with Privacy-Preserving Random Access
by: Chandar, Venkat, et al.
Published: (2025)

Necessity of Block Designs for Optimal Locally Private Distribution Estimation
by: Gentle, Abigail
Published: (2025)

Shuffling Cards When You Are of Very Little Brain: Low Memory Generation of Permutations
by: Menuhin, Boaz, et al.
Published: (2025)

Block Verification Accelerates Speculative Decoding
by: Sun, Ziteng, et al.
Published: (2024)

Differential privacy and Sublinear time are incompatible sometimes
by: Blocki, Jeremiah, et al.
Published: (2024)

Differential Privacy on Trust Graphs
by: Ghazi, Badih, et al.
Published: (2024)

A Bias-Accuracy-Privacy Trilemma for Statistical Estimation
by: Kamath, Gautam, et al.
Published: (2023)

Instance-Optimal Private Density Estimation in the Wasserstein Distance
by: Feldman, Vitaly, et al.
Published: (2024)

DPSW-Sketch: A Differentially Private Sketch Framework for Frequency Estimation over Sliding Windows (Technical Report)
by: Wang, Yiping, et al.
Published: (2024)

Improved Regret in Stochastic Decision-Theoretic Online Learning under Differential Privacy
by: Wu, Ruihan, et al.
Published: (2025)

Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies
by: Wang, Zhanyu, et al.
Published: (2022)

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

On the Price of Differential Privacy for Hierarchical Clustering
by: Deng, Chengyuan, et al.
Published: (2025)

Metric geometry of the privacy-utility tradeoff
by: Boedihardjo, March, et al.
Published: (2024)

Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
by: Zhao, Puning, et al.
Published: (2024)

Edgeworth Accountant: An Analytical Approach to Differential Privacy Composition
by: Wang, Hua, et al.
Published: (2022)

Coupling without Communication and Drafter-Invariant Speculative Decoding
by: Daliri, Majid, et al.
Published: (2024)

A Smooth Binary Mechanism for Efficient Private Continual Observation
by: Andersson, Joel Daniel, et al.
Published: (2023)

Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes
by: Peter, Naty, et al.
Published: (2023)

Epsilon*: Privacy Metric for Machine Learning Models
by: Negoescu, Diana M., et al.
Published: (2023)