ethics & community engagement

Purpose Statement

The overarching goal of Thrust 4 is to address the major ethical considerations needed in the identification, measurement, and analysis of sensitive data on migration, social mixing, and biometrics necessary for pandemic prevention using a health equity lens. We seek to ensure that all Thrusts are carried out in a way that respects the privacy, dignity and equitable treatment of individuals whose data we rely on to inform public health decisions. 

Pandemics disproportionately affect the most vulnerable, and can quickly exacerbate existing health inequities. The collection, analysis and application of mobility and social mixing data presents complex ethical challenges because this sensitive data presents unique privacy and surveillance concerns. Engaging communities, especially those that are most vulnerable to the disproportionate impact of pandemics, is crucial to developing an ethical scientific approach to preventing emerging pandemics.

Community Engagement at Brown

Building trust and rapport between MAPPS and the wider Brown University community is critical for two reasons:

1. We are asking Brown community members- first at the School of Public Health and later across the University- to contribute data to MAPPING@Brown, our proof-of-concept pilot project.

2. We are looking for community buy-in for a permanent Center for Mobility Analysis for Pandemic Prevention Strategies on campus.

Community engagement involves bidirectional, honest and ongoing communication between MAPPS and Brown affiliates about the risks and benefits of pandemic prevention research. MAPPS solicits community feedback in several forums described below.

In November 2022 public health students Lucas Blackmore, Emily Ma, Maria Pieruccini, and Mallory Go and project manager Peyton Luiz hosted focus group sessions with Brown students and staff. These conversations centered around the acceptability of a potential MAPPING@Brown study in which high-resolution indoor and outdoor location data would be collected on students, faculty, and staff to inform mathematical models of disease transmission. A substantial majority of respondents said they would enroll in such a study. A minority said they would be uncomfortable enrolling without more detailed information on data protections. The most frequently cited concern among students was the impact of a study app on phone battery life, while staff most frequently brought up re-identification concerns. Click here to read a complete summary of findings. 

Find opportunities to get involved with MAPPS and MAPPING@Brown here.

Dr. Netter and the SPH Community Entry Group oversee the MAPPING@Brown Community Advisory Board (CAB), which consists of SPH students, faculty, and staff to represent the interest of their peers. The CAB meets regularly to provide input on study design, recruitment approaches, data analysis and dissemination of findings.

Cryptography-based privacy preservation

With the input of her co-PIs and the support of her colleagues in computer science (CS), Dr. Anna Lysyanskaya investigates cryptographic approaches to data privacy preservation. At MAPPS’ Privacy and Ethics in Pandemic Data Collection and Processing Workshop in January 2023, Dr. Lysyanskaya led conversations with peer researchers about two subfields she believes could be transformative for public health research: differential privacy and multi-party computation. 

Differential privacy is a mathematically based method to determine whether or not a specific analysis puts individuals represented in the queried data set at risk of privacy loss. As Dr. Cynthia Dwork, one of the inventors of differential privacy, puts it:

“Roughly speaking, differential privacy ensures that the removal or addition of a single database item does not (substantially) affect the outcome of any analysis. It follows that no risk is incurred by joining the database, providing a mathematically rigorous means of coping with the fact that distributional information may be disclosive.” (2008)

Secure multi-party computation (MPC) protocols allow multiple participants to jointly analyze data without any party revealing their data to any other party. In more technical terms:

“Through MPC protocol, parties enter their data, which is then split into separate pieces and masked with other random numbers; the encoded data pieces are sent to multiple servers, assuring data privacy.” (Colarossi, 2019)

Pandemic research necessarily relies on data from a variety of sources, some of whom are likely to distrust others with sensitive information, such as mobility and social mixing patterns. MPC theoretically allows researchers and policy-makers to gain insight from a collection of data sets without having access to the raw data.

Dr. Lysyanskaya, Dr. Netter, and co-PI Dr. Thomas Trikalinos are investigating previous applications of differential privacy and MPC in epidemiological work and will collaborate with colleagues at Boston University and beyond to produce recommendations for future research directions. 

You can learn more about differential privacy with this tutorial by Boston University Professor of CS and friend of MAPPS Dr. Adam Smith. Dr. Smith delivered this lecture at the 2019 IEEE North American School of Information Theory.

Meet the SPH Community Entry Working Group

Co-PI Julia Netter (Adjunct Assistant Professor of the Practice of Computer Science & Visiting Assistant Professor of Philosophy) leads the SPH Community Entry Working Group. The group collaborates on research compliance, informational and recruitment activities, community feedback sessions, the MAPPING@Brown Community Advisory Board (CAB), and ethical study design. Members include: undergraduate students Thomas Cronin and Ishi Khurana; graduate students Nelson Anangwe, Maria Pieruccini, and Brooke Ury; and project management staff Peyton Luiz and Marta Wilson-Barthes.

Our work is supported by numerous volunteers who serve as CAB members and study recruitment ambassadors. Visit the team page for more information.