skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Wastewater Monitoring Raises Privacy and Ethical Considerations
Analysis of municipal wastewater, or sewage for public health applications is a rapidly expanding field aimed at understanding emerging epidemiological trends, including human and disease migration. The newly gained ability to extract and analyze genetic material from wastewater poses important societal and ethical questions, including: How to safeguard data? Who owns genetic data recovered from wastewater? What are the ethical and legal issues surrounding its use? In the U.S., both corporate and legal policies regarding privacy have been historically reactive instead of proactive. In wastewater-based epidemiology (WBE), the pace of innovation has outpaced the ability of social and legal mechanisms to keep up. To address this discrepancy, early and robust discussions of the research, policies, and ethics surrounding WBE analysis and genetics is needed. This paper contributes to this discussion by examining ownership issues for human genetic data recovered from wastewater and the uses to which it may be put. We focus particularly on the risks associated with personally identifiable data, highlighting potential risks, relevant privacy-enhancing technologies, and appropriate ethics. The paper proposes an approach for people conducting WBE studies to help them systematically consider the ethical and privacy implications of their work.  more » « less
Award ID(s):
1828010
PAR ID:
10277368
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
IEEE transactions on technology and society
ISSN:
2637-6415
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing)
    null (Ed.)
    User engagement with data privacy and security through consent banners has become a ubiquitous part of interacting with internet services. While previous work has addressed consent banners from either interaction design, legal, and ethics-focused perspectives, little research addresses the connections among multiple disciplinary approaches, including tensions and opportunities that transcend disciplinary boundaries. In this paper, we draw together perspectives and commentary from HCI, design, privacy and data protection, and legal research communities, using the language and strategies of “dark patterns” to perform an interaction criticism reading of three different types of consent banners. Our analysis builds upon designer, interface, user, and social context lenses to raise tensions and synergies that arise together in complex, contingent, and conflicting ways in the act of designing consent banners. We conclude with opportunities for transdisciplinary dialogue across legal, ethical, computer science, and interactive systems scholarship to translate matters of ethical concern into public policy. 
    more » « less
  2. ; ; ; (, Risk Analysis)
    Abstract Traditional health surveillance methods play a critical role in public health safety but are limited by the data collection speed, coverage, and resource requirements. Wastewater‐based epidemiology (WBE) has emerged as a cost‐effective and rapid tool for detecting infectious diseases through sewage analysis of disease biomarkers. Recent advances in big data analytics have enhanced public health monitoring by enabling predictive modeling and early risk detection. This paper explores the application of machine learning (ML) in WBE data analytics, with a focus on infectious disease surveillance and forecasting. We highlight the advantages of ML‐driven WBE prediction models, including their ability to process multimodal data, predict disease trends, and evaluate policy impacts through scenario simulations. We also examine challenges such as data quality, model interpretability, and integration with existing public health infrastructure. The integration of ML WBE data analytics enables rapid health data collection, analysis, and interpretation that are not feasible in current surveillance approaches. By leveraging ML and WBE, decision makers can reduce cognitive biases and enhance data‐driven responses to public health threats. As global health risks evolve, the synergy between WBE, ML, and data‐driven decision‐making holds significant potential for improving public health outcomes. 
    more » « less
  3. ; ; ; ; ; (, IEEE)
    Regulatory documents are complex and lengthy, making full compliance a challenging task for businesses. Similarly, privacy policies provided by vendors frequently fall short of the necessary legal standards due to insufficient detail. To address these issues, we propose a solution that leverages a Large Language Model (LLM) in combination with Semantic Web technology. This approach aims to clarify regulatory requirements and ensure that organizations’ privacy policies align with the relevant legal frameworks, ultimately simplifying the compliance process, reducing privacy risks, and improving efficiency. In this paper, we introduce a novel tool, the Privacy Policy Compliance Verification Knowledge Graph, referred to as PrivComp-KG. PrivComp-KG is designed to efficiently store and retrieve comprehensive information related to privacy policies, regulatory frameworks, and domain-specific legal knowledge. By utilizing LLM and Retrieval Augmented Generation (RAG), we can accurately identify relevant sections in privacy policies and map them to the corresponding regulatory rules. Our LLM-based retrieval system has demonstrated a high level of accuracy, achieving a correctness score of 0.9, outperforming other models in privacy policy analysis. The extracted information from individual privacy policies is then integrated into the PrivComp-KG. By combining this data with contextual domain knowledge and regulatory rules, PrivComp-KG can be queried to assess each vendor’s compliance with applicable regulations. We demonstrate the practical utility of PrivComp-KG by verifying the compliance of privacy policies across various organizations. This approach not only helps policy writers better understand legal requirements but also enables them to identify gaps in existing policies and update them in response to evolving regulations. 
    more » « less
  4. ; ; ; (, Springer Nature Switzerland)
    Electronic Health Records (EHRs) have become increasingly popular in recent years, providing a convenient way to store, manage and share relevant information among healthcare providers. However, as EHRs contain sensitive personal information, ensuring their security and privacy is most important. This paper reviews the key aspects of EHR security and privacy, including authentication, access control, data encryption, auditing, and risk management. Additionally, the paper dis- cusses the legal and ethical issues surrounding EHRs, such as patient consent, data ownership, and breaches of confidentiality. Effective implementation of security and privacy measures in EHR systems requires a multi-disciplinary approach involving healthcare providers, IT specialists, and regulatory bodies. Ultimately, the goal is to come upon a balance between protecting patient privacy and ensuring timely access to critical medical information for feature healthcare delivery. 
    more » « less
  5. ; ; ; ; ; ; (, Science of The Total Environment)
    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. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email