Saved in:
| Main Authors: | Sinh, Quentin, Ramon, Jan |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.01986 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Resampling methods for private statistical inference
by: Chadha, Karan, et al.
Published: (2024)
by: Chadha, Karan, et al.
Published: (2024)
Effective and secure federated online learning to rank
by: Wang, Shuyi
Published: (2024)
by: Wang, Shuyi
Published: (2024)
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training
by: Usynin, Dmitrii, et al.
Published: (2023)
by: Usynin, Dmitrii, et al.
Published: (2023)
Near-Optimal differentially private low-rank trace regression with guaranteed private initialization
by: Zha, Mengyue
Published: (2024)
by: Zha, Mengyue
Published: (2024)
Differentially private Bayesian tests
by: Chakraborty, Abhisek, et al.
Published: (2024)
by: Chakraborty, Abhisek, et al.
Published: (2024)
DP-SGD with weight clipping
by: Barczewski, Antoine, et al.
Published: (2023)
by: Barczewski, Antoine, et al.
Published: (2023)
Harnessing large-language models to generate private synthetic text
by: Kurakin, Alexey, et al.
Published: (2023)
by: Kurakin, Alexey, et al.
Published: (2023)
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration
by: Imakura, Akira, et al.
Published: (2024)
by: Imakura, Akira, et al.
Published: (2024)
Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning
by: Damie, Marc, et al.
Published: (2025)
by: Damie, Marc, et al.
Published: (2025)
How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations
by: Damie, Marc, et al.
Published: (2025)
by: Damie, Marc, et al.
Published: (2025)
Modulated learning for private and distributed regression with just a single sample per client device
by: Vepakomma, Praneeth, et al.
Published: (2026)
by: Vepakomma, Praneeth, et al.
Published: (2026)
5G enabled Mobile Edge Computing security for Autonomous Vehicles
by: D'Costa, Daryll Ralph, et al.
Published: (2022)
by: D'Costa, Daryll Ralph, et al.
Published: (2022)
Data value estimation on private gradients
by: Zhou, Zijian, et al.
Published: (2024)
by: Zhou, Zijian, et al.
Published: (2024)
SoK: Verifiable Cross-Silo FL
by: Korneev, Aleksei, et al.
Published: (2024)
by: Korneev, Aleksei, et al.
Published: (2024)
Differentially private and decentralized randomized power method
by: Nicolas, Julien, et al.
Published: (2024)
by: Nicolas, Julien, et al.
Published: (2024)
Training generative models from privatized data
by: Reshetova, Daria, et al.
Published: (2023)
by: Reshetova, Daria, et al.
Published: (2023)
The importance of feature preprocessing for differentially private linear optimization
by: Sun, Ziteng, et al.
Published: (2023)
by: Sun, Ziteng, et al.
Published: (2023)
Randomized algorithms for precise measurement of differentially-private, personalized recommendations
by: Laro, Allegra, et al.
Published: (2023)
by: Laro, Allegra, et al.
Published: (2023)
Privacy-preserving gradient-based fair federated learning
by: Adamek, Janis, et al.
Published: (2024)
by: Adamek, Janis, et al.
Published: (2024)
HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning
by: Khan, Momin Ahmad, et al.
Published: (2024)
by: Khan, Momin Ahmad, et al.
Published: (2024)
Quantum delegated and federated learning via quantum homomorphic encryption
by: Li, Weikang, et al.
Published: (2024)
by: Li, Weikang, et al.
Published: (2024)
Privacy utility trade offs for parameter estimation in degree heterogeneous higher order networks
by: Mandal, Bibhabasu, et al.
Published: (2026)
by: Mandal, Bibhabasu, et al.
Published: (2026)
FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy
by: Diaa, Abdulrahman, et al.
Published: (2024)
by: Diaa, Abdulrahman, et al.
Published: (2024)
Nonparametric extensions of randomized response for private confidence sets
by: Waudby-Smith, Ian, et al.
Published: (2022)
by: Waudby-Smith, Ian, et al.
Published: (2022)
Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
by: Saleem, Hamza, et al.
Published: (2024)
by: Saleem, Hamza, et al.
Published: (2024)
Towards Robust and Accurate Stability Estimation of Local Surrogate Models in Text-based Explainable AI
by: Burger, Christopher, et al.
Published: (2025)
by: Burger, Christopher, et al.
Published: (2025)
XBreaking: Understanding how LLMs security alignment can be broken
by: Arazzi, Marco, et al.
Published: (2025)
by: Arazzi, Marco, et al.
Published: (2025)
Ensuring superior learning outcomes and data security for authorized learner
by: Bang, Jeongho, et al.
Published: (2025)
by: Bang, Jeongho, et al.
Published: (2025)
The Effect of Similarity Measures on Accurate Stability Estimates for Local Surrogate Models in Text-based Explainable AI
by: Burger, Christopher, et al.
Published: (2024)
by: Burger, Christopher, et al.
Published: (2024)
Almost linear time differentially private release of synthetic graphs
by: Liu, Jingcheng, et al.
Published: (2024)
by: Liu, Jingcheng, et al.
Published: (2024)
A review of federated learning in renewable energy applications: Potential, challenges, and future directions
by: Grataloup, Albin, et al.
Published: (2023)
by: Grataloup, Albin, et al.
Published: (2023)
Uncertainty quantification by block bootstrap for differentially private stochastic gradient descent
by: Dette, Holger, et al.
Published: (2024)
by: Dette, Holger, et al.
Published: (2024)
Robustness of LLM-enabled vehicle trajectory prediction under data security threats
by: Wang, Feilong, et al.
Published: (2025)
by: Wang, Feilong, et al.
Published: (2025)
A GAN-based data poisoning framework against anomaly detection in vertical federated learning
by: Chen, Xiaolin, et al.
Published: (2024)
by: Chen, Xiaolin, et al.
Published: (2024)
DNA: Differentially private Neural Augmentation for contact tracing
by: Romijnders, Rob, et al.
Published: (2024)
by: Romijnders, Rob, et al.
Published: (2024)
IPFed: Identity protected federated learning for user authentication
by: Kaga, Yosuke, et al.
Published: (2024)
by: Kaga, Yosuke, et al.
Published: (2024)
Sampling-Free Privacy Accounting for Matrix Mechanisms under Random Allocation
by: Schuchardt, Jan, et al.
Published: (2026)
by: Schuchardt, Jan, et al.
Published: (2026)
Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.S
by: Badrinarayanan, Saikrishna, et al.
Published: (2024)
by: Badrinarayanan, Saikrishna, et al.
Published: (2024)
Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists
by: Ly, Timothée, et al.
Published: (2024)
by: Ly, Timothée, et al.
Published: (2024)
Bayes' capacity as a measure for reconstruction attacks in federated learning
by: Biswas, Sayan, et al.
Published: (2024)
by: Biswas, Sayan, et al.
Published: (2024)
Similar Items
-
Resampling methods for private statistical inference
by: Chadha, Karan, et al.
Published: (2024) -
Effective and secure federated online learning to rank
by: Wang, Shuyi
Published: (2024) -
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training
by: Usynin, Dmitrii, et al.
Published: (2023) -
Near-Optimal differentially private low-rank trace regression with guaranteed private initialization
by: Zha, Mengyue
Published: (2024) -
Differentially private Bayesian tests
by: Chakraborty, Abhisek, et al.
Published: (2024)