Verifiability and Privacy in Federated Learning through Context-Hiding Multi-Key Homomorphic Authenticators
Open Access
- 24 December 2025
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 1-6
Abstract
No abstract availableAll Related Versions
This publication has 15 references indexed in Scilit:
- An Overview of Trustworthy AI: Advances in IP Protection, Privacy-preserving Federated Learning, Security Verification, and GAI Safety AlignmentIEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2024
- LightVeriFL: A Lightweight and Verifiable Secure Aggregation for Federated LearningIEEE Journal on Selected Areas in Information Theory, 2024
- When the Curious Abandon Honesty: Federated Learning Is Not Private2023
- RoFL: Robustness of Secure Federated Learning2023
- Privacy and Robustness in Federated Learning: Attacks and DefensesIEEE Transactions on Neural Networks and Learning Systems, 2022
- Privacy-Preserving Distributed Support Vector Machines2021
- Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban InformaticsIEEE Transactions on Industrial Informatics, 2019
- Exploiting Unintended Feature Leakage in Collaborative Learning2019
- Context Hiding Multi-key Linearly Homomorphic Authenticators2019
- SecureML: A System for Scalable Privacy-Preserving Machine Learning2017