When an individual is harmed by someone in power, such as a workplace manager, it can help to identify allies--people who would offer sympathy, advice, or supportive action. However, ally discovery is fraught because the very people who might be most relevant--e.g., someone who reports to the same manager--might not be sympathetic and could potentially exacerbate the harm. We examine this problem in the specific context of PhD students navigating advising challenges and present a social media platform called "Moa" that brings together a number of features that we believe facilitate ally discovery. Moa's most novel element is an audience selection process that uses what we call consent boundaries, which allow users to flexibly define each post or comment's audience based on factors such as common social identity or lived experience, all while preserving anonymity--neither senders nor recipients learn each other's identities, even as the post reaches the right audience. A 3-week field study with 47 real-world users showed that the features in combination facilitated sensitive conversations about advising, with 22.6% of users using consent boundaries. We discuss both our overall "recipe" for systems for ally discovery and the benefits of a consent-centered approach to design.

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