A Framework and Decision Tool for Confidentiality Protection in Public Use Data

描述(申请人提供)：供公众使用的统计数据库是一个关键问题：如何在不泄露侵犯隐私、违反保密或危害国家安全的信息的情况下提供数据供分析。鉴于获取数据对于推进研究和制定政策至关重要，公共和私营部门的组织在这一保密问题上有很大的利害关系。然而，从数据中提取某些敏感信息的可能性可能会危及这些组织的福利，并可能危及它们所在社会的福利。因此，挑战是如何以一种能够准确分析数据的形式来表示数据，以支持研究、决策和政策举措，同时防止不择手段或恶意的一方利用数据产生有害后果。该项目的目标是开发一个实用的、基于计算机的框架，用于评估、测量和减轻公共使用数据中的披露风险。我们提出的框架，称为OptShield，克服了目前部署的披露限制方法中的缺点。我们通过将扰动和抑制方法与微数据级别的敏感记录的最佳切换相结合来实现这一点，以产生一种在保护机密性的同时保持数据完整性的方法。在第二阶段，我们提议继续进行算法和软件开发，以实现软件和服务的工作原型的目标。该软件将作为核心技术，为客户在保密保护方面具有重大利害关系的广阔市场提供应用程序。我们最终计划在第三阶段提供的应用程序将分三个阶段解决披露限制问题：(1)评估用户在组织的数据发布和共享计划中固有的定性和定量披露风险；(2)衡量用户建议的数据产品中的披露风险；以及(3)通过应用适当的披露限制技术来保护用户的数据。 与公共卫生相关：收集和共享敏感数据的公共卫生组织担心无意中泄露机密信息的风险，因为获取他们的数据对于推进研究和制定政策至关重要。然而，即使在个人身份标识被删除之后，从数据中提取某些易受攻击的信息元素的可能性也可能危及这些组织的福利，并可能危及它们所在社会的福利。例如，在美国卫生与公众服务部内，为了继续从美国人民和医疗保健提供者那里获取信息，保护记录的机密性是“首要问题”(CDC/NCHS保密指南)。OptTek Systems，Inc.(OptTek)正在开发一个全面的框架，旨在帮助公共卫生和其他组织避免公共使用数据中的机密信息泄露。该应用程序分三个阶段解决披露限制问题：(1)评估用户的定性和定量披露风险；(2)衡量用户提议的数据发布和共享计划中的披露风险；(3)通过应用适当的披露限制技术来保护用户的数据。

DESCRIPTION (provided by applicant): Statistical databases for public use pose a critical problem: how to make the data available for analysis without disclosing information that would infringe on privacy, violate confidentiality, or endanger national security. Organizations in the public and private sectors have a major stake in this confidentiality protection problem, given the fact that access to data is essential for advancing research and formulating policy. Yet, the possibility of extracting certain sensitive elements of information from the data can jeopardize the welfare of these organizations and potentially, the welfare of the society in which they operate. The challenge is, therefore, to represent the data in a form that permits accurate analysis for supporting research, decision-making and policy initiatives, while preventing an unscrupulous or ill- intentioned party from exploiting the data for harmful consequences. The objective of this project is to develop a practical, computer-based framework for assessing, measuring, and mitigating disclosure risk in public use data. Our proposed framework, called OptShield, overcomes the disadvantages found in currently deployed disclosure limitation methods. We achieve this by combining perturbation and suppression methods with optimal switching of sensitive records at the micro-data level, to produce a method that protects confidentiality while preserving data integrity. In Phase II we are proposing to continue algorithmic and software development to achieve the objective of a working prototype of the software and service. This software will serve as the core technology to provide an application for a broad market in which customers have a major stake in confidentiality protection. The application we ultimately plan to offer in Phase III will consist of a three-phased approach to the disclosure limitation problem: (1) Assess a user's qualitative and quantitative disclosure risks inherent in the organization's data publishing and sharing plans; (2) Measure the disclosure risks in a user's proposed data products; and (3) Protect the user's data by applying the appropriate disclosure limitation techniques. PUBLIC HEALTH RELEVANCE: Public health organizations that collect and share sensitive data are apprehensive about the risk of inadvertently disclosing confidential information, given the fact that access to their data is essential for advancing research and formulating policy. Yet, the possibility of extracting certain vulnerable elements of information from the data, even after personal identifiers have been removed, can jeopardize the welfare of these organizations and potentially the welfare of the society in which they operate. Within the US Department of Health and Human Services, for example, preserving the confidentiality of records in order to continue to elicit information from the American people and from health care providers is "a matter of primary concern" (CDC/NCHS confidentiality guide). OptTek Systems, Inc. (OptTek) is developing a comprehensive framework designed to help public health and other organizations to avoid the disclosure of confidential information in public-use data. The application consists of a three-phased approach to the disclosure limitation problem: (1) Assess a user's qualitative and quantitative disclosure risks; (2) Measure the disclosure risks in a user's proposed data publishing and sharing plans; and (3) Protect the user's data by applying the appropriate disclosure limitation techniques.