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

TC：大型：协作研究：匿名文本数据及其对实用性的影响

• 批准号: 1011984
• 负责人: Wei Jiang
• 金额: $ 35.7万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1011984&HistoricalAwards=false
• 关键词: TC; Large; Collaborative; Research; Anonymizing

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

豁免无法单独识别的数据的数据保护定律导致匿名研究爆炸。不幸的是，目前的去识别和匿名技术如何控制隐私和机密性的风险。匿名数据对现实世界应用程序的有用性也不是。该项目在三个方面解决了匿名：1）即使删除了显式标识符（名称，日期，位置），文本数据也可能包含高度可识别的信息。例如，印第安纳州患者护理网络（INPC）的主要投诉领域样本发现了几个“幻影肢体疼痛”的实例。可以在视觉上识别amputees，但是HIPAA安全港规则并未将其列为“识别信息”。任何明确列出所有标识数据的策略都可能失败。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，以从文本中删除特定细节，同时保留含义，消除这种高度可识别的信息，而无需先验地了解要识别的内容。 2）当前的匿名研究基于未经证实的可识别性措施。通过对合成数据的重新识别挑战（但基于实际的医疗保健数据），该项目正在评估这些措施的功效。跨学科的学生团队有挑战问题 - 带有假设医疗保健数据的匿名数据 - 并要求对个人健康信息进行（假设）推论。结果可用于校准不同匿名度量的有效性。 3）匿名数据的效用在研究中一直是一个关注：匿名数据是否提供可靠的研究结果？通过与金西研究所和普渡大学护理学院的医疗保健研究合作，该项目正在将原始数据的分析与匿名数据的分析进行比较，并评估匿名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：如果个人知道数据会被匿名化，他们的响应是否更加坦率？结果正在扩大可以在匿名数据上执行的研究范围，同时确保研究人员知道何时需要访问单独识别的数据（带有限制和保障措施）。通过这些任务，该项目正在促进我们利用现在收集的大量数据的能力，同时确保保护个人隐私。有关更多信息