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The rapidly expanding use of wastewater for public health surveillance requires new strategies to protect privacy rights, while data are collected at increasingly discrete geospatial scales, i.e., city, neighborhood, campus, and building-level. Data collected at high geospatial resolution can inform on labile, short-lived biomarkers, thereby making wastewater-derived data both more actionable and more likely to cause privacy concerns and stigma- tization of subpopulations. Additionally, data sharing restrictions among neighboring cities and communities can complicate efforts to balance public health protections with citizens’ privacy. Here, we have created an encrypted framework that facilitates the sharing of sensitive population health data among entities that lack trust for one another (e.g., between adjacent municipalities with different governance of health monitoring and data sharing). We demonstrate the utility of this approach with two real-world cases. Our results show the feasibility of sharing encrypted data between two municipalities and a laboratory, while performing secure private com- putations for wastewater-based epidemiology (WBE) with high precision, fast speeds, and low data costs. This framework is amenable to other computations used by WBE researchers including population normalized mass loads, fecal indicator normalizations, and quality control measures. The Centers for Disease Control and Pre- vention’s National Wastewater Surveillance System shows ~8 % of the records attributed to collection before the wastewater treatment plant, illustrating an opportunity to further expand currently limited community-level sampling and public health surveillance through security and responsible data-sharing as outlined here. 

This article presents CirFix, a framework for automatically repairing defects in hardware designs implemented in languages like Verilog. We propose a novel fault localization approach based on assignments to wires and registers, and a fitness function tailored to the hardware domain to bridge the gap between software-level automated program repair and hardware descriptions. We also present a benchmark suite of 32 defect scenarios corresponding to a variety of hardware projects. Overall, CirFix produces plausible repairs for 21/32 and correct repairs for 16/32 of the defect scenarios. Additionally, we evaluate CirFix's fault localization independently through a human study (n=41), and find that the approach may be a beneficial debugging aid for complex multi-line hardware defects.