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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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负责任的数据共享：识别并纠正可能的人类参与者重新识别。


从研究参与者收集的开放数据一方面在透明和共享的学术价值观与另一方面在隐私和安全之间造成了紧张关系。常见的解决方案是通过在共享之前删除个人识别信息（例如姓名或工作人员 ID）来使数据集匿名。然而，表面上匿名的数据集如果包含人口统计信息，则可能面临重新识别的风险。在本文中，我们为研究人员提供了广泛适用的指导和切实的工具，以便他们能够在不损害参与者隐私的情况下参与开放科学实践。具体来说，我们（a）审查当前的隐私标准，（b）描述计算机科学数据保护框架及其对社会科学的适应性，（c）为评估和解决重新识别风险提供实用指导，（d）引入两种开放式为心理科学家开发的源算法——MinBlur 和 MinBlurLite——在保持开放数据完整性的同时增强隐私性，并且 (e) 强调需要进一步关注的道德数据共享方面。最终，重新识别的风险不应阻止参与开放科学实践。相反，应该开发和利用技术创新，以便科学能够尽可能开放以促进透明度和共享，并尽可能封闭以维护隐私和安全。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。