:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Aliakbarpour, Maryam, Bairaktari, Konstantina, Smith, Adam, Swanberg, Marika, Ullman, Jonathan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Cryptography and Security
Online Access:	https://arxiv.org/abs/2412.12374
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Metalearning with Very Few Samples Per Task
by: Aliakbarpour, Maryam, et al.
Published: (2023)

Black-Box Privacy Attacks on Shared Representations in Multitask Learning
by: Abascal, John, et al.
Published: (2025)

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

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

High-Probability Bounds For Heterogeneous Local Differential Privacy
by: Aliakbarpour, Maryam, et al.
Published: (2025)

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

Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?
by: Swanberg, Marika, et al.
Published: (2025)

Lower Bounds for Public-Private Learning under Distribution Shift
by: Setlur, Amrith, et al.
Published: (2025)

A Unified Framework for Adversary-Aware Differential Privacy Bounds
by: Swanberg, Marika, et al.
Published: (2025)

Better Private Distribution Testing by Leveraging Unverified Auxiliary Data
by: Aliakbarpour, Maryam, et al.
Published: (2025)

TMI! Finetuned Models Leak Private Information from their Pretraining Data
by: Abascal, John, et al.
Published: (2023)

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

Health App Reviews for Privacy & Trust (HARPT): A Corpus for Analyzing Patient Privacy Concerns, Trust in Providers and Trust in Applications
by: Kelly, Timoteo, et al.
Published: (2025)

ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries
by: Cummings, Rachel, et al.
Published: (2024)

How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
by: Lowy, Andrew, et al.
Published: (2024)

Privacy-Preserving Optimal Parameter Selection for Collaborative Clustering
by: Ghasemian, Maryam, et al.
Published: (2024)

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

On the Privacy Risk of In-context Learning
by: Duan, Haonan, et al.
Published: (2024)

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

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

On the Privacy of Selection Mechanisms with Gaussian Noise
by: Lebensold, Jonathan, et al.
Published: (2024)

Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
by: Gao, Fengyu, et al.
Published: (2024)

Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
by: Fang, Congyu, et al.
Published: (2024)

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

Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning
by: Rafiei, Mina, et al.
Published: (2024)

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

Impact of Privacy Parameters on Deep Learning Models for Image Classification
by: Chaulagain, Basanta
Published: (2024)

Privacy Preserving In-Context-Learning Framework for Large Language Models
by: Bhusal, Bishnu, et al.
Published: (2025)

Privacy Attacks in Decentralized Learning
by: Mrini, Abdellah El, et al.
Published: (2024)

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

BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks
by: Poursiami, Hamed, et al.
Published: (2024)

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training
by: Zmushko, Philip, et al.
Published: (2024)

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)

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

Agentic Privacy-Preserving Machine Learning
by: Zhang, Mengyu, et al.
Published: (2025)

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

Preserving Privacy and Security in Federated Learning
by: Nguyen, Truc, et al.
Published: (2022)

A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises
by: Fotohi, Reza, et al.
Published: (2025)

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