III: Small: Safely Searching Among Sensitive Content

今天的搜索引擎主要是为了帮助人们找到他们想看的东西。奇怪的是，搜索引擎做得很好的事实意味着有许多集合不能被搜索。公民还不能搜索一些政府记录，因为混杂的信息可能需要保护。 出于类似的原因，学者们还不被允许看到大量积压的未经处理的档案收藏。这些限制以及更多限制是当前搜索引擎只能在敏感内容已被提前标记的情况下保护敏感内容这一事实的直接后果。 随着数字内容量的持续增加，基于手动查找和标记集合中的所有敏感内容的当前方法根本无法承受适应挑战的规模。该项目将通过创建一种新的搜索算法来应对这一挑战，这种算法旨在平衡搜索引擎在寻找相关内容方面的利益与内容提供商在保护敏感内容方面的利益。这项技术将极大地改变我们应对挑战的方式，从而造福社会，这些挑战包括政府透明度、个人和企业信息管理、民事诉讼和监管调查，以及学术界对档案材料的访问。该项目将利用评估驱动的信息检索技术来优化统一的目标函数，该目标函数在寻找相关内容的价值与保护敏感信息的必要性之间取得平衡。 这将需要制定一种新的评价措施，既要对价值（相关性）敏感，又要对成本（敏感性）敏感。 将使用析因小插曲调查技术来得出代表性应用程序的访问和限制的适当平衡。 调查结果将用于设计评价驱动的信息检索技术所依赖的功能集。最初的实验将在受保护的环境中进行，既包括本地实验，也包括对收集的共享任务评估，这些收集可以根据排除不适当披露的条款获得研究使用许可。最终，该项目寻求开发一个评估算法的过程，用于使用算法存款模型在敏感内容中搜索，其中可执行的搜索算法被发送到受保护的数据，只有手动审查的评估结果将返回给参与者。

Today's search engines are designed principally to help people find what they want to see. Paradoxically, the fact that search engines do this well means that there are many collections that can't be searched. Citizens can not yet search some government records because of intermixed information that may need to be protected. Scholars are not yet allowed to see much of the growing backlog of unprocessed archival collections for similar reasons. These limitations, and many more, are direct consequences of the fact that current search engines can only protect sensitive content if that sensitive content has been marked in advance. As the volume of digital content continues to increase, current approaches based on manually finding and marking all of the sensitive content in a collection simply cannot affordably accommodate the scale of the challenge. This project will address that challenge by creating a new class of search algorithms that are designed to balance the searcher's interest in finding relevant content with the content provider's interest in protecting sensitive content. This technology will benefit society by dramatically altering the way we approach challenges such as government transparency, personal and enterprise information management, civil litigation and regulatory investigations, and scholarly access to archival materials.The project will leverage evaluation-driven information retrieval techniques to optimize a unified objective function that balances the value of finding relevant content with the imperative to protect sensitive information. This will require developing a new class of evaluation measures that are sensitive to both value (relevance) and cost (sensitivity). Factorial vignette survey techniques will be used to elicit the context-appropriate balance of access and restriction for representative applications. The survey results will then be used to inform the design of the feature sets on which evaluation-driven information retrieval techniques depend. Initial experiments will be conducted in protected environments, both locally and as shared-task evaluations on collections that can be licensed for research use under terms that preclude inappropriate disclosure. Ultimately, the project seeks to develop a process for evaluating algorithms for search among sensitive content using an algorithm deposit model in which the executable search algorithm is sent to the protected data, and only manually vetted evaluation results will be returned to participants.