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要看到的东西。矛盾的是，搜索引擎在这方面做得很好，这意味着有许多集合无法被搜索。由于混杂的信息可能需要保护，公民还不能搜索一些政府记录。由于类似的原因，学者们还没有被允许看到大量积压的未经处理的档案。这些限制，以及更多的限制，是当前搜索引擎只能在敏感内容被事先标记的情况下保护敏感内容的直接后果。随着数字内容的数量不断增加，目前基于手动查找和标记集合中所有敏感内容的方法根本无法承受这一挑战的规模。该项目将通过创建一类新的搜索算法来解决这一挑战，这些算法旨在平衡搜索者对查找相关内容的兴趣与内容提供者对保护敏感内容的兴趣。这项技术将极大地改变我们应对挑战的方式，如政府透明度、个人和企业信息管理、民事诉讼和监管调查，以及对档案材料的学术访问，从而造福社会。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功能，以平衡查找相关内容的价值与保护敏感信息的必要性。这将需要发展对价值（相关性）和成本（敏感性）都敏感的一类新的评价措施。阶乘小插图调查技术将用于引出具有代表性的应用程序的访问和限制的上下文适当的平衡。然后，调查结果将用于通知评估驱动的信息检索技术所依赖的特征集的设计。最初的实验将在受保护的环境中进行，既可以在当地进行，也可以作为对收藏的共享任务评估，这些收藏可以根据防止不当披露的条款获得研究使用许可。最终，该项目寻求开发一种评估敏感内容搜索算法的过程，使用算法存储模型，其中可执行的搜索算法被发送到受保护的数据，只有人工审查的评估结果才会返回给参与者。

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.