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: 1012208
• 负责人: Victor Raskin
• 金额: $ 155.44万
• 依托单位: Purdue University
• 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 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1012208&HistoricalAwards=false
• 关键词: TC; Large; Collaborative; Research; Anonymizing

Data Protection laws that exempt data that is not individuallyidentifiable have led to an explosion in anonymization research.Unfortunately, how well current de-identification and anonymizationtechniques control risks to privacy and confidentiality is not wellunderstood. Neither is the usefulness of anonymized data for real-worldapplications. The project addresses anonymization on three fronts:1) Textual data, even when explicit identifiers are removed (names,dates, locations), can contain highly identifiable information. Forexample, a sample of chief complaint fields from the Indiana Networkfor Patient Care (INPC) found several instances of "phantom limbpain". Amputees can be visually identifiable, but the HIPAA SafeHarbor rules do not list this as "identifying information". Anypolicy explicitly listing all types of identifying data is likely tofail. Through a joint effort with computer science and linguistics,the project is developing new methods to remove specific details fromtext while preserving meaning, eliminating such highly identifiableinformation without a priori knowledge of what would be identifying.2) Current anonymization research is based on unproven measures ofidentifiability. Through a re-identification challenge on syntheticdata (but based on real healthcare data), the project is evaluatingthe efficacy of these measures. Interdisciplinary teams of studentsare given challenge problems - anonymized data with hypotheticalhealthcare data - and asked to make (hypothetical) inferences abouthealth information of individuals. The results can be used tocalibrate 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 partneringwith healthcare studies at the Kinsey Institute and Purdue UniversitySchool of Nursing, the project is comparing analyses on original datawith analyses on anonymized data, and evaluating the impact of typesof anonymization on research results. A related issue is determiningthe impact on data collection: Are individuals more candid in theirresponses if they know data will be anonymized? Outcomes are broadeningthe scope of research that can be performed on anonymized data, whileensuring that researchers know when access to individually identifiabledata (with attendant restrictions and safeguards) is needed.Through these tasks, the project is advancing our ability to utilizethe wealth of data we now collect for the benefit of society, whileensuring individual privacy is protected.For further information see the project web site at the URL:http://projects.cerias.purdue.edu/TextAnon

数据保护法豁免了不可单独识别的数据，这导致了匿名化研究的激增。不幸的是，目前的去身份化和匿名化技术如何很好地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 SafeHarbor 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政策都可能会失败。 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，从而在不先验了解识别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。 通过对合成数据的重新识别挑战（但基于真实的医疗保健数据），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中关注的问题：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？ 结果是扩大了可以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（附带限制和保护措施）。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，同时确保个人隐私受到保护。有关更多信息，请参阅该项目网站： 网址：http://projects.cerias.purdue.edu/TextAnon