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Related Experiment Video

Updated: Aug 19, 2025

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

Intelligent Pandemic Surveillance via Privacy-Preserving Crowdsensing.

Hafiz Asif¹, Periklis A Papakonstantinou¹, Stephanie Shiau²

¹MSIS Department, Rutgers University, New Jersey, USA.

IEEE Intelligent Systems

|December 5, 2022

Summary

This summary is machine-generated.

This study introduces a new privacy-preserving method for analyzing crowdsourced disease symptom data. It enables accurate, real-time insights for any region without compromising contributor privacy, crucial for pandemic response.

Keywords:

Covid-19 crowdsensing differential privacy disease surveillance pandemic

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Published on: April 9, 2021

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Published on: June 23, 2022

Area of Science:

Epidemiology
Data Science
Public Health

Background:

Accurate real-time data is crucial for pandemic response but often unavailable initially.
Crowdsensing spatially tagged disease symptoms offers an alternative data source.
Existing methods aggregate data for large regions, missing localized outbreaks and posing privacy risks.

Purpose of the Study:

To develop a novel, differentially private method for analyzing crowdsensed disease symptom data.
To enable accurate, real-time insights for user-defined regions of any size.
To ensure privacy protection for data contributors.

Main Methods:

Implementation of a novel differentially private algorithm for spatial data analysis.
Crowdsensing of spatially tagged disease-related symptoms.
Data aggregation and release for user-specified regions.

Main Results:

The proposed method provides accurate insights from crowdsensed data.
It allows for analysis at granular, user-defined geographical levels (e.g., neighborhoods).
The system successfully protects the privacy of individuals contributing data.

Conclusions:

The developed differentially private method enhances pandemic response capabilities.
It facilitates privacy-preserving intelligent systems for spatial and longitudinal data analytics.
This approach addresses limitations of traditional data aggregation methods in public health surveillance.