:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Tholoniat, Pierre, Kostopoulou, Kelly, Chowdhury, Mosharaf, Cidon, Asaf, Geambasu, Roxana, Lécuyer, Mathias, Yang, Junfeng
Format:	Preprint
Published:	2022
Subjects:	Cryptography and Security Machine Learning
Online Access:	https://arxiv.org/abs/2212.13228
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Cookie Monster: Efficient On-device Budgeting for Differentially-Private Ad-Measurement Systems
by: Tholoniat, Pierre, et al.
Published: (2024)

Big Bird: Resilient Privacy Budgeting Across Untrusted Web Domains
by: Tholoniat, Pierre, et al.
Published: (2025)

Engineering Robustness into Personal Agents with the AI Workflow Store
by: Geambasu, Roxana, et al.
Published: (2026)

Privacy Filters are Captured by Residues: A Characterization of Free Natural Filters and the Cost of Adaptivity
by: Regehr, Matthew, et al.
Published: (2026)

Differentially Private Training of Mixture of Experts Models
by: Tholoniat, Pierre, et al.
Published: (2024)

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

Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences
by: Lyu, Saiyue, et al.
Published: (2024)

Limits of Personalizing Differential Privacy Budgets
by: Cyffers, Edwige, et al.
Published: (2026)

Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion
by: Shpilevskiy, Frederick, et al.
Published: (2025)

ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?
by: Liu, Peihan, et al.
Published: (2026)

DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
by: Liu, Yu, et al.
Published: (2024)

On the Efficiency of Privacy Attacks in Federated Learning
by: Tabassum, Nawrin, et al.
Published: (2024)

CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference
by: Luo, Jinglong, et al.
Published: (2024)

FRAME : Comprehensive Risk Assessment Framework for Adversarial Machine Learning Threats
by: Shapira, Avishag, et al.
Published: (2025)

Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)

RAPID: Robust APT Detection and Investigation Using Context-Aware Deep Learning
by: Amaru, Yonatan, et al.
Published: (2024)

Real-World Adversarial Attacks on RF-Based Drone Detectors
by: Gazit, Omer, et al.
Published: (2025)

Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption
by: Chowdhury, Moontaha Nishat, et al.
Published: (2025)

Wasserstein Differential Privacy
by: Yang, Chengyi, et al.
Published: (2024)

A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
by: Shao, Jiawei, et al.
Published: (2023)

RuleGenie: SIEM Detection Rule Set Optimization
by: Shukla, Akansha, et al.
Published: (2025)

Rogue Cell: Adversarial Attack and Defense in Untrusted O-RAN Setup Exploiting the Traffic Steering xApp
by: Aizikovich, Eran, et al.
Published: (2025)

Detection of Compromised Functions in a Serverless Cloud Environment
by: Lavi, Danielle, et al.
Published: (2024)

Privacy Preserving Reinforcement Learning for Population Processes
by: Yang-Zhao, Samuel, et al.
Published: (2024)

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments
by: Li, Jianhui, et al.
Published: (2024)

Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
by: Liu, Zheyuan, et al.
Published: (2023)

Privacy Drift: Evolving Privacy Concerns in Incremental Learning
by: Ahamed, Sayyed Farid, et al.
Published: (2024)

$S^2$NeRF: Privacy-preserving Training Framework for NeRF
by: Zhang, Bokang, et al.
Published: (2024)

PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior
by: Flemings, James, et al.
Published: (2026)

Knowledge Return Oriented Prompting (KROP)
by: Martin, Jason, et al.
Published: (2024)

Synthetic Data: Revisiting the Privacy-Utility Trade-off
by: Sarmin, Fatima Jahan, et al.
Published: (2024)

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

KubeGuard: LLM-Assisted Kubernetes Hardening via Configuration Files and Runtime Logs Analysis
by: Cohen, Omri Sgan, et al.
Published: (2025)

PrivDiffuser: Privacy-Guided Diffusion Model for Data Obfuscation in Sensor Networks
by: Yang, Xin, et al.
Published: (2024)

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage
by: Meng, Jiayang, et al.
Published: (2025)

Empirical Privacy Variance
by: Hu, Yuzheng, et al.
Published: (2025)

FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation
by: Gu, Yuechun, et al.
Published: (2024)

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
by: Zhao, Tianyu, et al.
Published: (2025)

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
by: Pierquin, Clément, et al.
Published: (2023)

A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
by: Li, Xiang, et al.
Published: (2025)