Enhanced Consumer Healthcare Data Protection Through AI-Driven TinyML and Privacy-Preserving Techniques
In the recent digital landscape, securing healthcare data stored on personal devices has become imperative due to increasing cyberattacks. Healthcare data inside an organization is often vulnerable to security breaches and requires privacy-preserving mechanisms to ensure secure storage and sharing a...
Saved in:
| Main Authors: | S. Aanjankumar, Monoj Kumar Muchahari, Shabana Urooj, Ishmeet Kaur, Rajesh Kumar Dhanaraj, Hanan Abdullah Mengash, S. Poonkuntran, Parag Ravikant Kaveri |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11014071/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Empowering Healthcare: TinyML for Precise Lung Disease Classification
by: Youssef Abadade, et al.
Published: (2024-10-01) -
TinyML with Meta-Learning on Microcontrollers for Air Pollution Prediction
by: I Nyoman Kusuma Wardana, et al.
Published: (2024-04-01) -
Efficient Detection of Microplastics on Edge Devices With Tailored Compiler for TinyML Applications
by: Alessandro Cerioli, et al.
Published: (2025-01-01) -
Optimising TinyML with quantization and distillation of transformer and mamba models for indoor localisation on edge devices
by: Thanaphon Suwannaphong, et al.
Published: (2025-03-01) -
Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks
by: William Villegas-Ch, et al.
Published: (2024-01-01)