Copertina

Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity Recognition

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zakariyya, Idris, Tran, Linda, Sivangi, Kaushik Bhargav, Henderson, Paul, Deligianni, Fani
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computer Vision and Pattern Recognition Artificial Intelligence Cryptography and Security Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Differentially Private 2D Human Pose Estimation
di: Sivangi, Kaushik Bhargav, et al.
Pubblicazione: (2025)

Privacy-Preserving Federated Action Recognition via Differentially Private Selective Tuning and Efficient Communication
di: Zakariyya, Idris, et al.
Pubblicazione: (2026)

Artificial Intelligence-Driven Clinical Decision Support Systems
di: Alkan, Muhammet, et al.
Pubblicazione: (2025)

DP-RuL: Differentially-Private Rule Learning for Clinical Decision Support Systems
di: Lamp, Josephine, et al.
Pubblicazione: (2024)

DP-GENG : Differentially Private Dataset Distillation Guided by DP-Generated Data
di: Shi, Shuo, et al.
Pubblicazione: (2025)

DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum
di: Gu, Xiaolan, et al.
Pubblicazione: (2023)

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

DP-KAN: Differentially Private Kolmogorov-Arnold Networks
di: Kalinin, Nikita P., et al.
Pubblicazione: (2024)

Rethinking the Security of DP-SGD: A Corrected Analysis of Differentially Private Machine Learning
di: Wang, Wenhao, et al.
Pubblicazione: (2026)

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

DP-DocLDM: Differentially Private Document Image Generation using Latent Diffusion Models
di: Saifullah, Saifullah, et al.
Pubblicazione: (2025)

Private Data Leakage in Federated Human Activity Recognition for Wearable Healthcare Devices
di: Chen, Kongyang, et al.
Pubblicazione: (2024)

DP-BART for Privatized Text Rewriting under Local Differential Privacy
di: Igamberdiev, Timour, et al.
Pubblicazione: (2023)

DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis
di: Gong, Chen, et al.
Pubblicazione: (2026)

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

DP-SMOTE: Integrating Differential Privacy and Oversampling Technique to Preserve Privacy in Smart Homes
di: Elsayed, Amr Tarek, et al.
Pubblicazione: (2025)

Improving Parameter-Efficient Federated Learning with Differentially Private Refactorization
di: Tran, Linh, et al.
Pubblicazione: (2026)

Bilateral Differentially Private Vertical Federated Boosted Decision Trees
di: Zhang, Bokang, et al.
Pubblicazione: (2025)

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

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

Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
di: Sebag, Ilana, et al.
Pubblicazione: (2023)

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

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

Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
di: Pelikan, Martin, et al.
Pubblicazione: (2023)

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

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

Enhancing Accuracy-Privacy Trade-off in Differentially Private Split Learning
di: Pham, Ngoc Duy, et al.
Pubblicazione: (2023)

Preserving Target Distributions With Differentially Private Count Mechanisms
di: Kohli, Nitin, et al.
Pubblicazione: (2026)

SilentWood: Private Inference Over Gradient-Boosting Decision Forests
di: Ko, Ronny, et al.
Pubblicazione: (2024)

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

Onto-DP: Constructing Neighborhoods for Differential Privacy on Ontological Databases
di: Hayder, Yasmine, et al.
Pubblicazione: (2026)

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

S-BDT: Distributed Differentially Private Boosted Decision Trees
di: Peinemann, Thorsten, et al.
Pubblicazione: (2023)

DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
di: Liu, Yixuan, et al.
Pubblicazione: (2024)

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

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

Advances in Differential Privacy and Differentially Private Machine Learning
di: Das, Saswat, et al.
Pubblicazione: (2024)

Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent
di: Dufraiche, Jean, et al.
Pubblicazione: (2026)

Federated Split Learning for Human Activity Recognition with Differential Privacy
di: Ndeko, Josue, et al.
Pubblicazione: (2024)

Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks
di: Zakariyya, Idris, et al.
Pubblicazione: (2025)