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Large archival collections, such as email or government documents, must bemanually reviewed to identify any sensitive information before the collectioncan be released publicly. Sensitivity classification has received a lot ofattention in the literature. However, more recently, there has been increasinginterest in developing sensitivity-aware search engines that can provide userswith relevant search results, while ensuring that no sensitive documents arereturned to the user. Sensitivity-aware search would mitigate the need for amanual sensitivity review prior to collections being made available publicly.To develop such systems, there is a need for test collections that containrelevance assessments for a set of information needs as well as ground-truthlabels for a variety of sensitivity categories. The well-known Enron emailcollection contains a classification ground-truth that can be used to representsensitive information, e.g., the Purely Personal and Personal but inProfessional Context categories can be used to represent sensitive personalinformation. However, the existing Enron collection does not contain a set ofinformation needs and relevance assessments. In this work, we present acollection of fifty information needs (topics) with crowdsourced queryformulations (3 per topic) and relevance assessments (11,471 in total) for theEnron collection (mean number of relevant documents per topic = 11, variance =34.7). The developed information needs, queries and relevance judgements areavailable on GitHub and will be available along with the existing Enroncollection through the popular ir_datasets library. Our proposed collectionresults in the first freely available test collection for developingsensitivity-aware search systems.

SARA: A Collection of Sensitivity-Aware Relevance Assessments