Sensitive Patient

Sensitive patient data management in healthcare is a critical area focusing on balancing the need for data-driven advancements in medicine with robust privacy protections. Current research emphasizes developing and evaluating methods like federated learning, differential privacy, and homomorphic encryption to enable collaborative data analysis without compromising individual identities. These techniques, often implemented with transformer models and other deep learning architectures, are crucial for advancing AI in healthcare while adhering to ethical and legal standards. The ultimate goal is to facilitate medical research, improve diagnostics and treatment, and build trust in data-driven healthcare systems.

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 10, 2024

CL3: A Collaborative Learning Framework for the Medical Data Ensuring Data Privacy in the Hyperconnected Environment
Mohamamd Zavid Parvez, Rafiqul Islam, Md Zahidul Islam
Transfer Learning Incremental Learning Collaborative Learning Global Model Healthcare Data Medical Data Sensitive Patient

October 1, 2024

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

September 27, 2024

In-depth Analysis of Privacy Threats in Federated Learning for Medical Data
Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu
Medical Image Privacy Risk Medical Data Depth Analysis Sensitive Patient Private Training Privacy Threat

September 14, 2024

Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare
Zhikai Li, Jing Zhang, Qingyi Gu
Segment Anything Model Quantization Operator Healthcare System Low Bit Quantization Sensitive Patient Data Free Quantization

September 12, 2024

Controllable Synthetic Clinical Note Generation with Privacy Guarantees
Tal Baumel, Andre Manoel, Daniel Jones, Shize Su, Huseyin Inan, Aaron (Ari)Bornstein, Robert Sim
Differential Privacy Privacy Guarantee Medical Domain Medical Datasets Sensitive Patient Domain Annotation

September 11, 2024

SoK: Security and Privacy Risks of Medical AI
Yuanhaur Chang, Han Liu, Evin Jaff, Chenyang Lu, Ning Zhang
Adversarial Attack Security Related Privacy Risk Medical AI Sensitive Patient Medical Artificial Intelligence

July 3, 2024

Membership Inference Attacks Against Time-Series Models
Noam Koren, Abigail Goldsteen, Guy Amit, Ariel Farkash
Time Series Membership Inference Attack Privacy Risk Sensitive Patient

June 15, 2024

Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
Yukai Xu, Jingfeng Zhang, Yujie Gu
Heterogeneous Federated Learning Sensitive Patient Differential Privacy Mechanism Model Confidentiality

May 22, 2024

Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review
Md Shahin Ali, Md Manjurul Ahsan, Lamia Tasnim, Sadia Afrin, Koushik Biswas, Md Maruf Hossain, Md Mahfuz Ahmed, Ronok Hashan, Md Khairul Islam, Shivakumar Raman
Technical Challenge Data Driven Systematic Review Healthcare System Decentralized Data Sensitive Patient Healthcare Application Model Behavior

May 19, 2024

Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework
Daniel Commey, Sena Hounsinou, Garth V. Crosby
Differential Privacy Federated Learning Blockchain Based Platform Healthcare Data Blockchain Network Sensitive Patient

May 10, 2024

Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare
Xingyu Li, Lu Peng, Yuping Wang, Weihua Zhang
Machine Learning Foundation Model Federated Learning Emerging Opportunity Open Challenge Biomedical Research Sensitive Patient Complex Model Federated Foundation Model

March 20, 2024

Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?
Ileana Montoya Perez, Parisa Movahedi, Valtteri Nieminen, Antti Airola, Tapio Pahikkala
Synthetic Data Sensitive Patient

March 15, 2024

Medical Unlearnable Examples: Securing Medical Data from Unauthorized Traning via Sparsity-Aware Local Masking
Weixiang Sun, Yixin Liu, Zhiling Yan, Kaidi Xu, Lichao Sun
Healthcare Data Sensitive Patient Feature Masking Medical Artificial Intelligence Unlearnable Example Sparse Feature Secure Collaborative Learning

March 14, 2024

Empowering Healthcare through Privacy-Preserving MRI Analysis
Al Amin, Kamrul Hasan, Saleh Zein-Sabatto, Deo Chimba, Liang Hong, Imtiaz Ahmed, Tariqul Islam
Deep Learning Healthcare System Sensitive Patient Robust Ensemble Federated Ensemble

March 13, 2024

Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records
Erlend Frayling, Jake Lever, Graham McDonald
Zero Shot Synthetic Data Patient Data Sensitive Patient Shot Natural Language Generation Synthetic Clinical Note

March 5, 2024

Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures
Samuel Kim, Min Sang Kim
Automatic Detection Sensitive Patient Personalized Treatment Clopidogrel Treatment Failure

February 16, 2024

Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection
Ziyu Wang, Zhongqi Yang, Iman Azimi, Amir M. Rahmani
Differential Privacy Case Study Mental Health Stress Detection Daily Living Sensitive Patient

January 31, 2024

A primer on synthetic health data
Jennifer Anne Bartell, Sander Boisen Valentin, Anders Krogh, Henning Langberg, Martin Bøgsted
Synthetic Data Sensitive Patient Synthetic Health Data

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

Sensitive Patient

Papers

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

CL3: A Collaborative Learning Framework for the Medical Data Ensuring Data Privacy in the Hyperconnected Environment

The Gradient of Health Data Privacy

In-depth Analysis of Privacy Threats in Federated Learning for Medical Data

Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare

Controllable Synthetic Clinical Note Generation with Privacy Guarantees

SoK: Security and Privacy Risks of Medical AI

Membership Inference Attacks Against Time-Series Models

Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data

Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review

Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework

Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare

Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?

Medical Unlearnable Examples: Securing Medical Data from Unauthorized Traning via Sparsity-Aware Local Masking

Empowering Healthcare through Privacy-Preserving MRI Analysis

Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records

Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures

Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection

A primer on synthetic health data

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