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

数据保护法豁免了无法单独识别的数据，这导致了匿名化研究的爆炸式增长。不幸的是，目前的去识别和匿名技术如何控制隐私和机密性风险尚不清楚。匿名数据对于现实世界的应用程序也没有什么用处。该项目从三个方面解决匿名问题：1）即使删除了明确的标识符（姓名、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 (INNPC) 的主诉字段样本发现了数起“幻肢痛”的病例。截肢者可以通过视觉识别，但 HIPAA 安全港规则并未将其列为“识别信息”。任何明确列出所有类型的识别数据的策略都可能会失败。通过与计算机科学和语言学的共同努力，该项目正在开发新方法，从文本中删除特定细节，同时保留含义，从而在不事先了解要识别的内容的情况下消除此类高度可识别的信息。 2) 当前的匿名化研究基于未经证实的可识别性措施。通过对合成数据（但基于真实医疗数据）的重新识别挑战，该项目正在评估这些措施的有效性。跨学科的学生团队面临挑战问题 - 匿名数据和假设的医疗保健数据 - 并要求对个人的健康信息做出（假设的）推论。结果可用于校准不同匿名措施的有效性。 3）匿名数据的效用一直是研究关注的问题：匿名数据能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，该项目正在将原始数据分析与匿名数据分析进行比较，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：如果个人知道数据将被匿名，他们的反应是否会更加坦诚？结果扩大了可对匿名数据进行的研究范围，同时确保研究人员知道何时需要访问个人可识别数据（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现在收集的大量数据造福社会的能力，同时确保个人隐私受到保护。有关更多信息，请参阅项目网站，网址为：http://projects.cerias.purdue.edu/TextAnon