Cover Image

Auditing $f$-Differential Privacy in One Run

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mahloujifar, Saeed, Melis, Luca, Chaudhuri, Kamalika
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

Observational Auditing of Label Privacy
by: Kalemaj, Iden, et al.
Published: (2025)

Machine Learning with Privacy for Protected Attributes
by: Mahloujifar, Saeed, et al.
Published: (2025)

Privacy Amplification for the Gaussian Mechanism via Bounded Support
by: Hu, Shengyuan, et al.
Published: (2024)

SecAlign: Defending Against Prompt Injection with Preference Optimization
by: Chen, Sizhe, et al.
Published: (2024)

How Well Can Differential Privacy Be Audited in One Run?
by: Keinan, Amit, et al.
Published: (2025)

Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds
by: Chaudhuri, Kamalika, et al.
Published: (2024)

DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy
by: Wang, Erchi, et al.
Published: (2026)

On Differentially Private U Statistics
by: Chaudhuri, Kamalika, et al.
Published: (2024)

Sequentially Auditing Differential Privacy
by: González, Tomás, et al.
Published: (2025)

Auditing Differential Privacy Guarantees Using Density Estimation
by: Koskela, Antti, et al.
Published: (2024)

Auditing Differential Privacy in the Black-Box Setting
by: Shi, Kaining, et al.
Published: (2025)

Tight Privacy Audit in One Run
by: Xiang, Zihang, et al.
Published: (2025)

Communication-Efficient Triangle Counting under Local Differential Privacy
by: Imola, Jacob, et al.
Published: (2021)

Metric Differential Privacy at the User-Level Via the Earth Mover's Distance
by: Imola, Jacob, et al.
Published: (2024)

CIMemories: A Compositional Benchmark for Contextual Integrity of Persistent Memory in LLMs
by: Mireshghallah, Niloofar, et al.
Published: (2025)

Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference
by: Liu, Terrance, et al.
Published: (2025)

Tight Auditing of Differential Privacy in MST and AIM
by: Ganev, Georgi, et al.
Published: (2026)

Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
by: Aliakbarpour, Maryam, et al.
Published: (2024)

Managing Correlations in Data and Privacy Demand
by: Chaudhuri, Syomantak, et al.
Published: (2025)

Can We Infer Confidential Properties of Training Data from LLMs?
by: Huang, Pengrun, et al.
Published: (2025)

Privacy Audit as Bits Transmission: (Im)possibilities for Audit by One Run
by: Xiang, Zihang, et al.
Published: (2025)

A New Analysis of Differential Privacy's Generalization Guarantees
by: Jung, Christopher, et al.
Published: (2019)

Private Estimation when Data and Privacy Demands are Correlated
by: Chaudhuri, Syomantak, et al.
Published: (2024)

Visual Privacy Auditing with Diffusion Models
by: Schwethelm, Kristian, et al.
Published: (2024)

Private Fine-tuning of Large Language Models with Zeroth-order Optimization
by: Tang, Xinyu, et al.
Published: (2024)

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

Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
by: Jin, Richeng, et al.
Published: (2023)

Evaluating Privacy Leakage in Split Learning
by: Qiu, Xinchi, et al.
Published: (2023)

Optimizing Canaries for Privacy Auditing with Metagradient Descent
by: Boglioni, Matteo, et al.
Published: (2025)

The Sample Complexity of Membership Inference and Privacy Auditing
by: Haghifam, Mahdi, et al.
Published: (2025)

FairProof : Confidential and Certifiable Fairness for Neural Networks
by: Yadav, Chhavi, et al.
Published: (2024)

ExpProof : Operationalizing Explanations for Confidential Models with ZKPs
by: Yadav, Chhavi, et al.
Published: (2025)

Noisy Neighbors: Efficient membership inference attacks against LLMs
by: Galli, Filippo, et al.
Published: (2024)

Measuring Privacy Loss in Distributed Spatio-Temporal Data
by: Koga, Tatsuki, et al.
Published: (2024)

RL Is a Hammer and LLMs Are Nails: A Simple Reinforcement Learning Recipe for Strong Prompt Injection
by: Wen, Yuxin, et al.
Published: (2025)

Privacy Blur: Quantifying Privacy and Utility for Image Data Release
by: Mahloujifar, Saeed, et al.
Published: (2025)

Auditing Privacy Mechanisms via Label Inference Attacks
by: Busa-Fekete, Róbert István, et al.
Published: (2024)

Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy
by: Koga, Tatsuki, et al.
Published: (2024)

PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
by: Kazmi, Mishaal, et al.
Published: (2024)

Publicly-Detectable Watermarking for Language Models
by: Fairoze, Jaiden, et al.
Published: (2023)