Image de couverture de livre

Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Rafiei, Mina, Maheri, Mohammadmahdi, Rabiee, Hamid R.
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Cryptography and Security
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Automated Privacy-Preserving Techniques via Meta-Learning
par: Carvalho, Tânia, et autres
Publié: (2024)

Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning
par: Maheri, Mohammad Mahdi, et autres
Publié: (2025)

Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning
par: Alsulaimawi, Zahir
Publié: (2024)

TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks
par: Maheri, Mohammad M, et autres
Publié: (2025)

Verifiable Unlearning on Edge
par: Maheri, Mohammad M, et autres
Publié: (2025)

Privacy Against Agnostic Inference Attacks in Vertical Federated Learning
par: Varasteh, Morteza
Publié: (2023)

Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning
par: Yang, Hansi, et autres
Publié: (2024)

Noise-Aware Differentially Private Regression via Meta-Learning
par: Räisä, Ossi, et autres
Publié: (2024)

Adversarial Attacks on Graph Neural Networks via Meta Learning
par: Zügner, Daniel, et autres
Publié: (2019)

Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations
par: Trizna, Dmitrijs
Publié: (2022)

Privacy in Metalearning and Multitask Learning: Modeling and Separations
par: Aliakbarpour, Maryam, et autres
Publié: (2024)

SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges
par: Panzade, Prajwal, et autres
Publié: (2022)

Privacy Drift: Evolving Privacy Concerns in Incremental Learning
par: Ahamed, Sayyed Farid, et autres
Publié: (2024)

SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendations
par: Tahera, Mohoshin Ara, et autres
Publié: (2026)

On the Privacy Risk of In-context Learning
par: Duan, Haonan, et autres
Publié: (2024)

Privacy Attacks in Decentralized Learning
par: Mrini, Abdellah El, et autres
Publié: (2024)

Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning
par: Zhou, Shicheng, et autres
Publié: (2024)

PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
par: Kazmi, Mishaal, et autres
Publié: (2024)

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

A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises
par: Fotohi, Reza, et autres
Publié: (2025)

On the Efficiency of Privacy Attacks in Federated Learning
par: Tabassum, Nawrin, et autres
Publié: (2024)

Agentic Privacy-Preserving Machine Learning
par: Zhang, Mengyu, et autres
Publié: (2025)

Machine Learning with Privacy for Protected Attributes
par: Mahloujifar, Saeed, et autres
Publié: (2025)

Preserving Privacy and Security in Federated Learning
par: Nguyen, Truc, et autres
Publié: (2022)

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training
par: Zmushko, Philip, et autres
Publié: (2024)

Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge
par: Bertoli, Gustavo de Carvalho
Publié: (2026)

A User-Centric, Privacy-Preserving, and Verifiable Ecosystem for Personal Data Management and Utilization
par: Zafar, Osama, et autres
Publié: (2025)

FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation
par: Gu, Yuechun, et autres
Publié: (2024)

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
par: Zhao, Tianyu, et autres
Publié: (2025)

EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
par: Bostani, Hamid, et autres
Publié: (2021)

Privacy Preserving Reinforcement Learning for Population Processes
par: Yang-Zhao, Samuel, et autres
Publié: (2024)

Survey of Privacy Threats and Countermeasures in Federated Learning
par: Hayashitani, Masahiro, et autres
Publié: (2024)

Evaluations of Machine Learning Privacy Defenses are Misleading
par: Aerni, Michael, et autres
Publié: (2024)

Privacy Side Channels in Machine Learning Systems
par: Debenedetti, Edoardo, et autres
Publié: (2023)

Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
par: Mozaffari, Hamid, et autres
Publié: (2024)

Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research
par: Zafar, Osama, et autres
Publié: (2025)

A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
par: Li, Xiang, et autres
Publié: (2025)

Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
par: Schuchardt, Jan, et autres
Publié: (2025)

ZK-APEX: Zero-Knowledge Approximate Personalized Unlearning with Executable Proofs
par: Maheri, Mohammad M, et autres
Publié: (2025)

WARP: Weight Teleportation for Attack-Resilient Unlearning Protocols
par: Maheri, Mohammad M, et autres
Publié: (2025)