SHF: Small: Collaborative Research: Preserving Test Coverage While Achieving Data Anonymity for Database-Centric Applications

软件工程师通常无法访问机密数据，因为拥有这些数据的组织制定了内部安全规则，并且有几项法律规定了数据保护和隐私。这种情况使基本的软件工程任务（如测试）变得复杂。为了提供对所需数据的访问权限，数据所有者通常使用商业工具来匿名或“净化”数据。不幸的是，现有的工具都没有考虑到基本的软件工程任务，如测试，这导致了匿名数据对软件工程师几乎没有价值的情况。目前，软件工程师很少或根本没有有意义的数据，这是创建高质量软件的巨大障碍。该研究计划解决了软件工程的一个基本问题：数据所有者如何保护私人信息，以便数据主体（例如，人员、设备）不能被重新识别，而数据保持其对软件工程任务的有效性？为了保持数据对软件工程任务的有用性，需要考虑应用程序结构的算法。这项工作将为软件工程和数据隐私之间的相互作用的新研究方向奠定基础，PI将通过一套低成本软件开发和进化的工具来支持它。

Software engineers often do not get access to confidential data because of internal security rules that are put in place by organizations that own these data and because of several laws that regulate data protection and privacy. This situation complicates basic software engineering tasks such as testing. To give some access to required data, data owners typically use a commercial tool to anonymize or "sanitize" the data. Unfortunately, none of the existing tools takes into account basic software engineering tasks such as testing, which leads to situations where the anonymized data is of little to no value for software engineers. Currently, software engineers operate with little or no meaningful data, which is a great obstacle to creating high quality software. This research program addresses a fundamental question of software engineering: how can a data owner protect private information so that the data subjects (e.g., persons, equipment) cannot be re-identified while the data retains their efficacy for software engineering tasks? To preserve the usefulness of data for software engineering tasks, algorithms are needed that take into account the structures of the applications. This work will lay a foundation for a new direction of research on interactions between software engineering and data privacy, and the PIs will support it with a set of tools for low-cost software development and evolution.