Encrypted data-sharing for preserving privacy in wastewater-based epidemiology.

Area of Science:

• Environmental Science
• Public Health
• Computer Science

Background:

• Wastewater surveillance is expanding to granular levels (building, campus), increasing data sensitivity and privacy risks.
• Sharing high-resolution wastewater data between entities with differing data governance policies presents privacy and trust challenges.
• Existing data sharing restrictions can hinder comprehensive public health surveillance efforts.

Purpose of the Study:

• To develop and demonstrate an encrypted framework for secure data sharing in wastewater-based epidemiology (WBE).
• To enable collaboration between untrusted entities (e.g., municipalities, labs) for sensitive health data.
• To balance public health surveillance needs with robust privacy protections for subpopulations.

Main Methods:

• Developed a novel encrypted framework for secure data sharing among multiple entities.
• Implemented and tested the framework using two real-world case studies involving municipalities and a laboratory.
• Validated the framework's ability to perform secure private computations for WBE.

Main Results:

• Demonstrated the feasibility of securely sharing encrypted wastewater data between two municipalities and a laboratory.
• Achieved high precision, fast computation speeds, and low data costs for secure private computations in WBE.
• Showcased the framework's compatibility with essential WBE computations like normalization and quality control.

Conclusions:

• The encrypted framework effectively facilitates secure data sharing and private computations for wastewater-based epidemiology.
• This approach enhances public health surveillance capabilities by enabling responsible data sharing at community levels.
• The framework supports the expansion of wastewater surveillance, addressing privacy concerns and improving data utility.