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

数据保护法豁免了不能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，目前的去识别和匿名化技术在多大程度上控制了隐私和机密性风险，我们还没有得到很好的理解。匿名数据对于现实世界的应用的有用性也是如此。该项目从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），也可以包含高度可识别的信息。例如，印第安纳州患者护理网络（INPC）的主诉领域样本发现了几个“幻肢痛”的例子。截肢者可以在视觉上被识别，但HIPAA安全港规则并没有将其列为“识别信息”。任何明确列出所有类型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，从文本中删除特定细节，同时保留意义，在没有先验知识的情况下删除这些高度可识别的信息。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。跨学科的学生团队被赋予挑战问题——匿名数据和假设的医疗数据——并被要求对个人的健康信息做出（假设的）推断。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关注的问题：匿名数据是否提供可信的研究结果？通过与金赛研究所（Kinsey Institute）和普渡大学护理学院（Purdue UniversitySchool of Nursing）的医疗研究机构合作，该项目正在比较对原始数据的分析与对匿名数据的分析，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坦诚？结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可识别的数据（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，同时确保个人隐私得到保护。欲了解更多信息，请参阅项目网站的URL:http://projects.cerias.purdue.edu/TextAnon

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