Responsible Data Sharing: Identifying and Remedying Possible Re-Identification of Human Participants

Open data collected from research participants creates a tension between scholarly values of transparency and sharing, on the one hand, and privacy and security, on the other hand. A common solution is to make data sets anonymous by removing personally identifying information (e.g., names or worker IDs) before sharing. However, ostensibly anonymized data sets may be at risk of re-identification 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.