Private Means and the Curious Incident of the Free Lunch
Fuente:
arXiv
Saved in:
| Main Authors: | Fitzsimons, Jack, Honaker, James, Shoemate, Michael, Singhal, Vikrant |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions
by: Singhal, Vikrant
Published: (2023)
by: Singhal, Vikrant
Published: (2023)
Naturally Private Recommendations with Determinantal Point Processes
by: Fitzsimons, Jack, et al.
Published: (2024)
by: Fitzsimons, Jack, et al.
Published: (2024)
Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning
by: Rinberg, Roy, et al.
Published: (2025)
by: Rinberg, Roy, et al.
Published: (2025)
Not All Learnable Distribution Classes are Privately Learnable
by: Bun, Mark, et al.
Published: (2024)
by: Bun, Mark, et al.
Published: (2024)
No Free Lunch with Guardrails
by: Kumar, Divyanshu, et al.
Published: (2025)
by: Kumar, Divyanshu, et al.
Published: (2025)
Privacy in Theory, Bugs in Practice: Grey-Box Auditing of Differential Privacy Libraries
by: Cebere, Tudor, et al.
Published: (2026)
by: Cebere, Tudor, et al.
Published: (2026)
No Free Lunch Theorem for Privacy-Preserving LLM Inference
by: Zhang, Xiaojin, et al.
Published: (2024)
by: Zhang, Xiaojin, et al.
Published: (2024)
No Free Lunch for Defending Against Prefilling Attack by In-Context Learning
by: Xue, Zhiyu, et al.
Published: (2024)
by: Xue, Zhiyu, et al.
Published: (2024)
Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy
by: Covington, Christian, et al.
Published: (2021)
by: Covington, Christian, et al.
Published: (2021)
Uniformity Testing under User-Level Local Privacy
by: Canonne, Clément L., et al.
Published: (2025)
by: Canonne, Clément L., et al.
Published: (2025)
AAA: an Adaptive Mechanism for Locally Differential Private Mean Estimation
by: Wei, Fei, et al.
Published: (2024)
by: Wei, Fei, et al.
Published: (2024)
PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps
by: Liu, Ruixuan, et al.
Published: (2024)
by: Liu, Ruixuan, et al.
Published: (2024)
No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices
by: Pang, Qi, et al.
Published: (2024)
by: Pang, Qi, et al.
Published: (2024)
Improving Count-Mean Sketch as the Leading Locally Differentially Private Frequency Estimator for Large Dictionaries
by: Pan, Mingen
Published: (2024)
by: Pan, Mingen
Published: (2024)
Almost-Free Queue Jumping for Prior Inputs in Private Neural Inference
by: Zhang, Qiao, et al.
Published: (2026)
by: Zhang, Qiao, et al.
Published: (2026)
Alignment of Cybersecurity Incident Prioritisation with Incident Response Management Maturity Capabilities
by: Gulay, Abdulaziz, et al.
Published: (2024)
by: Gulay, Abdulaziz, et al.
Published: (2024)
Inferentially-Private Private Information
by: Wang, Shuaiqi, et al.
Published: (2024)
by: Wang, Shuaiqi, et al.
Published: (2024)
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates
by: Yue, Kai, et al.
Published: (2025)
by: Yue, Kai, et al.
Published: (2025)
Breaking Free: Efficient Multi-Party Private Set Union Without Non-Collusion Assumptions
by: Dong, Minglang, et al.
Published: (2024)
by: Dong, Minglang, et al.
Published: (2024)
Differentially Private Federated $k$-Means Clustering with Server-Side Data
by: Scott, Jonathan, et al.
Published: (2025)
by: Scott, Jonathan, et al.
Published: (2025)
Liquefaction: Privately Liquefying Blockchain Assets
by: Austgen, James, et al.
Published: (2024)
by: Austgen, James, et al.
Published: (2024)
Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters
by: Haney, Samuel, et al.
Published: (2023)
by: Haney, Samuel, et al.
Published: (2023)
Private Map-Secure Reduce: Infrastructure for Efficient AI Data Markets
by: Wagh, Sameer, et al.
Published: (2025)
by: Wagh, Sameer, et al.
Published: (2025)
Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework
by: Chen, Shengchao, et al.
Published: (2024)
by: Chen, Shengchao, et al.
Published: (2024)
Transportation Cyber Incident Awareness through Generative AI-Based Incident Analysis and Retrieval-Augmented Question-Answering Systems
by: Thomas, Ostonya, et al.
Published: (2025)
by: Thomas, Ostonya, et al.
Published: (2025)
Employing LLMs for Incident Response Planning and Review
by: Hays, Sam, et al.
Published: (2024)
by: Hays, Sam, et al.
Published: (2024)
Optimal Survey Design for Private Mean Estimation
by: Chen, Yu-Wei, et al.
Published: (2025)
by: Chen, Yu-Wei, et al.
Published: (2025)
An Attack-Driven Incident Response and Defense System (ADIRDS)
by: Lai, Anthony Cheuk Tung, et al.
Published: (2025)
by: Lai, Anthony Cheuk Tung, et al.
Published: (2025)
IRCopilot: Automated Incident Response with Large Language Models
by: Lin, Xihuan, et al.
Published: (2025)
by: Lin, Xihuan, et al.
Published: (2025)
Auditing Private Prediction
by: Chadha, Karan, et al.
Published: (2024)
by: Chadha, Karan, et al.
Published: (2024)
Private Data Imputation
by: Kati, Abdelkarim, et al.
Published: (2025)
by: Kati, Abdelkarim, et al.
Published: (2025)
Private Count Release: A Simple and Scalable Approach for Private Data Analytics
by: Rogers, Ryan
Published: (2024)
by: Rogers, Ryan
Published: (2024)
Half-Moon Cookie: Private, Similarity-Based Blocklisting with TOCTOU-Attack Resilience
by: Zhang, Xinyuan, et al.
Published: (2026)
by: Zhang, Xinyuan, et al.
Published: (2026)
Towards Incident Response Orchestration and Automation for the Advanced Metering Infrastructure
by: Lekidis, Alexios, et al.
Published: (2024)
by: Lekidis, Alexios, et al.
Published: (2024)
SOCpilot: Verifying Policy Compliance for LLM-Assisted Incident Response
by: Barbieri, Sidnei, et al.
Published: (2026)
by: Barbieri, Sidnei, et al.
Published: (2026)
FIST: A Structured Threat Modeling Framework for Fraud Incidents
by: Dai, Yu-Chen, et al.
Published: (2025)
by: Dai, Yu-Chen, et al.
Published: (2025)
Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning
by: Ball, Marshall, et al.
Published: (2024)
by: Ball, Marshall, et al.
Published: (2024)
Accelerating Incident Response: A Hybrid Approach for Data Breach Reporting
by: Arrus, Aurora, et al.
Published: (2026)
by: Arrus, Aurora, et al.
Published: (2026)
Enterprise Security Incident Analysis and Countermeasures Based on the T-Mobile Data Breach
by: Cui, Zhuohan, et al.
Published: (2025)
by: Cui, Zhuohan, et al.
Published: (2025)
PLAN: Variance-Aware Private Mean Estimation
by: Aumüller, Martin, et al.
Published: (2023)
by: Aumüller, Martin, et al.
Published: (2023)
Similar Items
-
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions
by: Singhal, Vikrant
Published: (2023) -
Naturally Private Recommendations with Determinantal Point Processes
by: Fitzsimons, Jack, et al.
Published: (2024) -
Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning
by: Rinberg, Roy, et al.
Published: (2025) -
Not All Learnable Distribution Classes are Privately Learnable
by: Bun, Mark, et al.
Published: (2024) -
No Free Lunch with Guardrails
by: Kumar, Divyanshu, et al.
Published: (2025)