TC:Large:Collaborative Research:Anonymizing Textual Data and its Impact on Utility

数据保护法豁免了无法识别个人身份的数据，这导致了匿名化研究的爆炸式增长。不幸的是，目前的去身份化和匿名化技术如何控制隐私和机密性风险还没有得到很好的理解。匿名数据对于现实世界的应用也没有用处。该项目在三个方面解决了匿名化问题：1）文本数据，即使删除了显式标识符（姓名，日期，位置），也可能包含高度可识别的信息。例如，来自印第安纳州病人护理网络（INPC）的一个主要投诉领域的样本发现了几个“幻肢痛”的例子。截肢者可以在视觉上识别，但HIPAA安全港规则没有将其列为“识别信息”。任何明确列出所有类型识别数据的政策都可能失败。通过与计算机科学和语言学的共同努力，该项目正在开发新的方法，从文本中删除特定的细节，同时保留含义，消除这种高度可识别的信息，而无需先验知识来识别。2)目前的匿名化研究是基于未经证实的可识别性措施。通过对合成数据（但基于真实的医疗保健数据）的重新识别挑战，该项目正在评估这些措施的效力。跨学科的学生团队被赋予挑战性的问题-匿名数据与假设的医疗保健数据-并要求作出（假设）推断个人的健康信息。研究结果可用于校准不同匿名化措施的有效性。3)匿名数据的效用一直是研究中的一个问题：匿名数据是否提供可信的研究结果？通过与金赛研究所和普渡大学护理学院的医疗保健研究合作，该项目将对原始数据的分析与对匿名数据的分析进行比较，并评估匿名化类型对研究结果的影响。一个相关的问题是确定对数据收集的影响：如果个人知道数据将被匿名化，他们的回答是否会更加坦率？这些成果扩大了可以对匿名数据进行研究的范围，同时确保研究人员知道何时需要访问个人身份数据（附带限制和保障措施）。通过这些任务，该项目正在提高我们利用我们现在收集的大量数据造福社会的能力，同时确保个人隐私得到保护。欲了解更多信息，请访问项目网站，网址为：http://projects.cerias.purdue.edu/TextAnon

Data Protection laws that exempt data that is not individually identifiable have led to an explosion in anonymization research. Unfortunately, how well current de-identification and anonymization techniques control risks to privacy and confidentiality is not well understood. Neither is the usefulness of anonymized data for real-world applications. The project addresses anonymization on three fronts: 1) Textual data, even when explicit identifiers are removed (names, dates, locations), can contain highly identifiable information. For example, a sample of chief complaint fields from the Indiana Network for Patient Care (INPC) found several instances of "phantom limb pain". Amputees can be visually identifiable, but the HIPAA Safe Harbor rules do not list this as "identifying information". Any policy explicitly listing all types of identifying data is likely to fail. Through a joint effort with computer science and linguistics, the project is developing new methods to remove specific details from text while preserving meaning, eliminating such highly identifiable information without a priori knowledge of what would be identifying. 2) Current anonymization research is based on unproven measures of identifiability. Through a re-identification challenge on synthetic data (but based on real healthcare data), the project is evaluating the efficacy of these measures. Interdisciplinary teams of students are given challenge problems - anonymized data with hypothetical healthcare data - and asked to make (hypothetical) inferences about health information of individuals. The results can be used to calibrate the effectiveness of different anonymization measures. 3) The utility of anonymized data has been a concern among research: Does anonymized data provide credible research results? By partnering with healthcare studies at the Kinsey Institute and Purdue University School of Nursing, the project is comparing analyses on original data with analyses on anonymized data, and evaluating the impact of types of anonymization on research results. A related issue is determining the impact on data collection: Are individuals more candid in their responses if they know data will be anonymized? Outcomes are broadening the scope of research that can be performed on anonymized data, while ensuring that researchers know when access to individually identifiable data (with attendant restrictions and safeguards) is needed. Through these tasks, the project is advancing our ability to utilize the wealth of data we now collect for the benefit of society, while ensuring individual privacy is protected. For further information see the project web site at the URL: http://projects.cerias.purdue.edu/TextAnon