Work

Responsible data sharing: Identifying and remedying possible re-identification of human participants.

Year: 2024

Type: article

Abstract: if they include demographic information. In the present article, we provide researchers with broadly applicable guidance and tangible tools so that they can engage in open science practices without jeopardizing participants' privacy. Specifically, we (a) review current privacy standards, (b) describe computer science data protection frameworks and their adaptability to the social sciences, (c) provide practical guidance for assessing and addressing re-identification risk, (d) introduce two open-source algorithms developed for psychological scientists-MinBlur and MinBlurLite-to increase privacy while maintaining the integrity of open data, and (e) highlight aspects of ethical data sharing that require further attention. Ultimately, the risk of re-identification should not dissuade engagement with open science practices. Instead, technical innovations should be developed and harnessed so that science can be as open as possible to promote transparency and sharing and as closed as necessary to maintain privacy and security. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Source: American Psychologist

DOI: 10.1037/amp0001346

Authors: Kirsten N. Morehouse, Benedek Kurdi, Brian A. Nosek

Institutions: Harvard University, University of Illinois Urbana-Champaign, University of Virginia

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4396670802

FWCI: 3.095

Cites: 19

Cited by: 22

Related to: 10