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Pathcheck
  • PathCheck: Privacy-Preserving Crowdsourcing for Citizen Engagement
  • Background
  • Problem
  • Stakeholders
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    • The Three Step Process
    • How do we solve the grand challenge?
    • Unique parts of our solution
    • Core Technical Innovation
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  • Privacy Preserved AI for HealthCare
    • Exposure Notification for Automated Contact Tracing (Download only solution)
    • Risk Calculator
    • Secure Multi-Party Computation
    • Differential Privacy
    • Federated and Split Learning
  • Solution Videos
  • The PathCheck Foundation
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Problem

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Last updated 4 years ago

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Our goal is to fill the gaps in public health knowledge by crowdsourcing data from citizens to improve pandemic responses.

In a pandemic, data is one of the most important tools for policymakers, public health officials, and health system agents. Citizens need to have easy access to exposure alerts, symptoms, testing, treatment, and vaccination, but much of this journey is not shared with public health and remains invisible to the health system.

These gaps in data gathering, especially about activities, contacts, and status of citizens are one of the key challenges for an effective response. The data-scarce planning leads to inefficiencies, lives lost, and socio-economic costs.

Technological app-based solutions can be used to fill in gaps, but they currently suffer from two key problems that make citizens uncooperative and disengaged:

  1. A lack of incentives for users to adopt and engage with the app

  2. Citizen’s privacy concerns and the fear of the government becoming a surveillance state.

How can we achieve crowdsourcing, privacy, and personalized engagement in a pandemic? And how can we simultaneously provide planning tools for public health by making citizen data available in real-time?