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Updated: Jan 19, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

A privacy-preserving distributed filtering framework for NLP artifacts.

Md Nazmus Sadat^1,2, Md Momin Al Aziz^3,4, Noman Mohammed³

¹Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada. sadat@cs.umanitoba.ca.

BMC Medical Informatics and Decision Making

|September 9, 2019

Summary

This summary is machine-generated.

A new secure protocol uses private set intersection and secure thresholding to filter sensitive sentences in clinical notes, enhancing medical data sharing while preserving data utility for research.

Keywords:

Biomedical data security and privacy Clinical notes de-identification Homomorphic encryption

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Data Privacy and Consumer Information Protection

Data Privacy and Consumer Information Protection

Area of Science:

Biomedical informatics
Data privacy
Secure computation

Background:

Medical data sharing faces significant privacy challenges, hindering collaborative research.
De-identifying clinical notes automatically is difficult, necessitating alternative privacy-preserving methods.
Removing sentences with potential personal information is a viable strategy.

Purpose of the Study:

To develop a secure protocol for identifying and filtering sensitive sentences in distributed clinical notes.
To enhance medical data privacy without compromising data utility for research.
To extend existing frequency-based filtering methods with advanced security considerations.

Main Methods:

Developed a novel secure protocol utilizing private set intersection and secure thresholding.
Identified uncommon and low-frequency terms to guide sentence filtering in distributed datasets.
Employed homomorphic encryption for secure multi-party computation.

Main Results:

The proposed framework is scalable with respect to the number of data owners and dataset size.
Experimental results confirm the method's efficiency and ability to retain data utility for analysis.
Demonstrated the feasibility of using homomorphic encryption for secure collaborative analysis.

Conclusions:

The developed secure protocol effectively addresses privacy concerns in medical data sharing.
The method enables secure identification and filtering of sensitive information in clinical notes.
This approach facilitates collaborative research by balancing privacy protection and data utility.