The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Futuristic Image Processing Techniques to Ameliorate Data Security and Privacy in Kidney Health Studies
|
|
Author(s): Lakshya Pandey (VIT Bhopal University, India), Spandan Agrawal (VIT Bhopal University, India), D. Lakshmi (VIT Bhopal University, India)and Ananya Chandorkar (Walt Disney Company, USA)
Copyright: 2027
Pages: 22
Source title:
Managing Sensitive Health Data Through Federated Learning and Generative AI: Privacy Preserving Techniques
Source Author(s)/Editor(s): Manisha Guduri (Lawrence Technological University, USA), George Pappas (Lawrence Technological University, USA)and Sandeep Thota (Oracle Inc., USA)
DOI: 10.4018/979-8-3373-7426-0.ch015
Purchase
|
Abstract
Deep Learning (DL) is well-suited for handling big data challenges and future issues in IoT. Advanced image processing techniques are pivotal in addressing security challenges in medical imaging. Anonymization techniques, encryption, watermarking, and Federated Learning (FL) are crucial for securing data and protecting privacy. AI-driven Clinical Decision Support Systems (CDSS) are increasingly used in clinical medicine, integrating Machine Learning (ML) capabilities into expert systems to enhance performance. The Unlearning Technique has recently become a way to implement the concept of “the right to be forgotten” in FL. Current research on effective retraining or approximate unlearning methods often overlooks the information leakage risks tied to model differences before and after unlearning.
Related Content
|
.
© 2027.
48 pages.
|
|
.
© 2027.
36 pages.
|
|
.
© 2027.
26 pages.
|
|
.
© 2027.
22 pages.
|
|
.
© 2027.
36 pages.
|
|
.
© 2027.
30 pages.
|
|
.
© 2027.
30 pages.
|
|
|