Cover Image

DP-KAN: Differentially Private Kolmogorov-Arnold Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kalinin, Nikita P., Bombari, Simone, Zakerinia, Hossein, Lampert, Christoph H.
Format:	Preprint
Published:	2024
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

Banded Square Root Matrix Factorization for Differentially Private Model Training
by: Kalinin, Nikita P., et al.
Published: (2024)

Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD
by: Kalinin, Nikita P., et al.
Published: (2025)

Privacy for Free in the Overparameterized Regime
by: Bombari, Simone, et al.
Published: (2024)

Differentially Private Federated $k$-Means Clustering with Server-Side Data
by: Scott, Jonathan, et al.
Published: (2025)

Normalized Square Root: Sharper Matrix Factorization Bounds for Differentially Private Continual Counting
by: Henzinger, Monika, et al.
Published: (2025)

DP-LDMs: Differentially Private Latent Diffusion Models
by: Liu, Michael F., et al.
Published: (2023)

DP-TLDM: Differentially Private Tabular Latent Diffusion Model
by: Zhu, Chaoyi, et al.
Published: (2024)

Optimizing IoT Threat Detection with Kolmogorov-Arnold Networks (KANs)
by: Emelianova, Natalia, et al.
Published: (2025)

Sampling-Free Privacy Accounting for Matrix Mechanisms under Random Allocation
by: Schuchardt, Jan, et al.
Published: (2026)

Training Set Reconstruction from Differentially Private Forests: How Effective is DP?
by: Gorgé, Alice, et al.
Published: (2025)

DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models
by: Sha, Haichao, et al.
Published: (2026)

FlashDP: Private Training Large Language Models with Efficient DP-SGD
by: Wang, Liangyu, et al.
Published: (2025)

From Privacy to Generalization: Linear Max-Information Bounds for DP-SGD
by: Lampert, Christoph H., et al.
Published: (2026)

Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
by: Ngo, Hoang M., et al.
Published: (2026)

DP-SPRT: Differentially Private Sequential Probability Ratio Tests
by: Michel, Thomas, et al.
Published: (2025)

DP-TabICL: In-Context Learning with Differentially Private Tabular Data
by: Carey, Alycia N., et al.
Published: (2024)

Too Good to be True? Turn Any Model Differentially Private With DP-Weights
by: Zagardo, David
Published: (2024)

A Kolmogorov-Arnold Network for Explainable Detection of Cyberattacks on EV Chargers
by: Saber, Ahmad Mohammad, et al.
Published: (2025)

A Kolmogorov-Arnold Network for Interpretable Cyberattack Detection in AGC Systems
by: Jilan, Jehad, et al.
Published: (2025)

Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?
by: Dong, Andy, et al.
Published: (2026)

Population Risk Bounds for Kolmogorov-Arnold Networks Trained by DP-SGD with Correlated Noise
by: Wang, Puyu, et al.
Published: (2026)

LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
by: Lim, Juntaek, et al.
Published: (2024)

LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models
by: Yang, Qin, et al.
Published: (2024)

KAN-LSTM: Benchmarking Kolmogorov-Arnold Networks for Cyber Security Threat Detection in IoT Networks
by: Hassanin, Mohammed
Published: (2026)

DP-SNP-TIHMM: Differentially Private, Time-Inhomogeneous Hidden Markov Models for Synthesizing Genome-Wide Association Datasets
by: Rahimian, Shadi, et al.
Published: (2025)

F-KANs: Federated Kolmogorov-Arnold Networks
by: Zeydan, Engin, et al.
Published: (2024)

SMA-DP: Spectral Memory-Aware Differential Privacy for Deep Learning
by: Partohaghighi, Mohammad, et al.
Published: (2026)

How Private are DP-SGD Implementations?
by: Chua, Lynn, et al.
Published: (2024)

DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization
by: Wang, Lionel Z., et al.
Published: (2026)

DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training
by: Kalinin, Nikita P., et al.
Published: (2026)

Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning
by: Fu, Xiaowen, et al.
Published: (2024)

Delving into Differentially Private Transformer
by: Ding, Youlong, et al.
Published: (2024)

Differentially Private Attention Computation
by: Gao, Yeqi, et al.
Published: (2023)

Differentially Private Diffusion Models
by: Dockhorn, Tim, et al.
Published: (2022)

Privately Learning Decision Lists and a Differentially Private Winnow
by: Bun, Mark, et al.
Published: (2026)

Correlated Noise Mechanisms for Differentially Private Learning
by: Pillutla, Krishna, et al.
Published: (2025)

Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
by: Zhang, Meifan, et al.
Published: (2024)

Differentially Private Random Feature Model
by: Liao, Chunyang, et al.
Published: (2024)

Differentially Private Conditional Independence Testing
by: Kalemaj, Iden, et al.
Published: (2023)

FairDP: Certified Fairness with Differential Privacy
by: Tran, Khang, et al.
Published: (2023)