Patient Privacy

Protecting patient privacy in the age of digital health is paramount, focusing on balancing data utility with individual rights. Current research emphasizes developing privacy-preserving techniques, including federated learning, fully homomorphic encryption, and generative AI models for synthetic data creation, alongside novel anonymization methods like deep learning-based image obfuscation and face swapping. These advancements aim to enable data-driven healthcare advancements, such as improved diagnostics and AI-powered tools, while mitigating risks of data breaches and re-identification. The ultimate goal is to foster a trustworthy healthcare ecosystem that leverages data effectively without compromising patient confidentiality.

Papers

November 10, 2024

In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages
Joseph Gatto, Parker Seegmiller, Timothy E. Burdick, Sarah Masud Preum
New Framework Context Learning Patient Friendly Language Sensitive Patient Healthcare Workflow Patient Privacy Synthetic Patient Physician Dialogue

October 16, 2024

De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy
Moritz Rempe, Lukas Heine, Constantin Seibold, Fabian Hörst, Jens Kleesiek
Medical Image Data Medical Data De Identification Anonymization Technique Patient Privacy

October 1, 2024

The Gradient of Health Data Privacy
Baihan Lin
Natural Gradient Sensitive Patient Digital Healthcare Data Governance Patient Privacy

February 14, 2024

Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis
Kyungsu Kim, Junhyun Park, Saul Langarica, Adham Mahmoud Alkhadrawi, Synho Do
Contrastive Learning Case Study ChatGPT Generated Conversation Radiology Report Healthcare Data Cloud Based Patient Privacy

January 17, 2024

MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical Images with Transformers and Fully Homomorphic Encryption
Prajwal Panzade, Daniel Takabi, Zhipeng Cai
Fine Tuning Transformer Megatron Decepticons Homomorphic Encryption Biomedical Image Sensitive Patient Image Encryption Patient Privacy

October 14, 2023

Multimodal Federated Learning in Healthcare: a Review
Jacob Thrasher, Alina Devkota, Prasiddha Siwakotai, Rohit Chivukula, Pranav Poudel, Chaunbo Hu, Binod Bhattarai, Prashnna Gyawali
Federated Learning Narrative Review Healthcare System Medical Domain Sensitive Patient Patient Privacy

May 9, 2023

Leveraging Generative AI Models for Synthetic Data Generation in Healthcare: Balancing Research and Privacy
Aryan Jadon, Shashank Kumar
Synthetic Data Synthetic Data Generation Privacy Policy Healthcare System DH Research Patient Privacy

April 16, 2023

Ontology for Healthcare Artificial Intelligence Privacy in Brazil
Tiago Andres Vaz, José Miguel Silva Dora, Luís da Cunha Lamb, Suzi Alves Camey
Top Level Ontology Healthcare Data Consistent Anonymization Effect Privacy Model Patient Privacy

November 9, 2022

Framework Construction of an Adversarial Federated Transfer Learning Classifier
Hang Yi, Tongxuan Bie, Tongjiang Yan
Generative Adversarial Network Transfer Learning New Framework Image Classification Federated Transfer Learning Patient Privacy

September 23, 2022

Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacy
Kai Packhäuser, Sebastian Gündel, Florian Thamm, Felix Denzinger, Andreas Maier
Whistleblower Re Identification Chest Radiograph Anonymization Technique Patient Privacy ChestX Ray8 Dataset

August 21, 2022

Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images
S. Maryam Hosseini, Milad Sikaroudi, Morteza Babaei, H. R. Tizhoosh
Differential Privacy Histopathology Image Cluster Structure Secure Approach Private Training Secure Multiparty Computation Party Computation Patient Privacy

April 7, 2022

Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy
Ethan Wilson, Frederick Shic, Jenny Skytta, Eakta Jain
Face Swapping Swap Distance Minimization Face Recognition Accuracy Patient Privacy Masquerade Attack

January 23, 2022

A Survey on Patients Privacy Protection with Stganography and Visual Encryption
Hussein K. Alzubaidy, Dhiah Al-Shammary, Mohammed Hamzah Abed
Timely Survey Full Model Steganographic Performance Patient Privacy

Patient Privacy

Papers

In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages

De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy

The Gradient of Health Data Privacy

Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis

MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical Images with Transformers and Fully Homomorphic Encryption

Multimodal Federated Learning in Healthcare: a Review

Leveraging Generative AI Models for Synthetic Data Generation in Healthcare: Balancing Research and Privacy

Ontology for Healthcare Artificial Intelligence Privacy in Brazil

Framework Construction of an Adversarial Federated Transfer Learning Classifier

Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacy

Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images

Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy

A Survey on Patients Privacy Protection with Stganography and Visual Encryption