Cover Image

Black-Box Privacy Attacks on Shared Representations in Multitask Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Abascal, John, Berrios, Nicolás, Oprea, Alina, Ullman, Jonathan, Smith, Adam, Jagielski, Matthew
Format:	Preprint
Published:	2025
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

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

Privacy in Metalearning and Multitask Learning: Modeling and Separations
by: Aliakbarpour, Maryam, et al.
Published: (2024)

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

Phantom: General Backdoor Attacks on Retrieval Augmented Language Generation
by: Chaudhari, Harsh, et al.
Published: (2024)

Thought-Transfer: Indirect Targeted Poisoning Attacks on Chain-of-Thought Reasoning Models
by: Chaudhari, Harsh, et al.
Published: (2026)

Cascading Adversarial Bias from Injection to Distillation in Language Models
by: Chaudhari, Harsh, et al.
Published: (2025)

Adversarial Inception Backdoor Attacks against Reinforcement Learning
by: Rathbun, Ethan, et al.
Published: (2024)

SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents
by: Rathbun, Ethan, et al.
Published: (2024)

Covert Attacks on Machine Learning Training in Passively Secure MPC
by: Jagielski, Matthew, et al.
Published: (2025)

Backdoor Attacks in Peer-to-Peer Federated Learning
by: Syros, Georgios, et al.
Published: (2023)

Attacks and Mitigations for Distributed Governance of Agentic AI under Byzantine Adversaries
by: Laws, Matthew D., et al.
Published: (2026)

Beware Untrusted Simulators -- Reward-Free Backdoor Attacks in Reinforcement Learning
by: Rathbun, Ethan, et al.
Published: (2026)

One-shot Empirical Privacy Estimation for Federated Learning
by: Andrew, Galen, et al.
Published: (2023)

Privacy Side Channels in Machine Learning Systems
by: Debenedetti, Edoardo, et al.
Published: (2023)

Differentially Private Prototypes for Imbalanced Transfer Learning
by: Wahdany, Dariush, et al.
Published: (2024)

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

Reconstruction of Personally Identifiable Information from Supervised Finetuned Models
by: Furukawa, Sae, et al.
Published: (2026)

Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning
by: Bao, Huan, et al.
Published: (2024)

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

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

UTrace: Poisoning Forensics for Private Collaborative Learning
by: Rose, Evan, et al.
Published: (2024)

BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack
by: Vo, Viet Quoc, et al.
Published: (2024)

Tricking Retrievers with Influential Tokens: An Efficient Black-Box Corpus Poisoning Attack
by: Wang, Cheng, et al.
Published: (2025)

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

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

Syntax- and Compilation-Preserving Evasion of LLM Vulnerability Detectors
by: Sun, Luze, et al.
Published: (2026)

GRAPHTEXTACK: A Realistic Black-Box Node Injection Attack on LLM-Enhanced GNNs
by: Ma, Jiaji, et al.
Published: (2025)

Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable Confidence
by: Hong, Hanbin, et al.
Published: (2023)

User Inference Attacks on Large Language Models
by: Kandpal, Nikhil, et al.
Published: (2023)

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

Targeted Data Poisoning for Black-Box Audio Datasets Ownership Verification
by: Bouaziz, Wassim, et al.
Published: (2025)

Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks
by: Noorbakhsh, Sayedeh Leila, et al.
Published: (2024)

Universal Black-Box Reward Poisoning Attack against Offline Reinforcement Learning
by: Xu, Yinglun, et al.
Published: (2024)

SoK: Data Minimization in Machine Learning
by: Staab, Robin, et al.
Published: (2025)

Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks
by: Poursiami, Hamed, et al.
Published: (2025)

FRIDA: Free-Rider Detection using Privacy Attacks
by: Recasens, Pol G., et al.
Published: (2024)

Text-to-Image Models Leave Identifiable Signatures: Implications for Leaderboard Security
by: Naseh, Ali, et al.
Published: (2025)

Exploiting Leaderboards for Large-Scale Distribution of Malicious Models
by: Suri, Anshuman, et al.
Published: (2025)

Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem
by: Lin, Shuyi, et al.
Published: (2025)

Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)