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Ren Yi

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2 papers

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TMLR Journal 2025 Journal Article

Privacy Awareness for Information-Sharing Assistants: A Case-study on Form-filling with Contextual Integrity

Sahra Ghalebikesabi
Eugene Bagdasarian
Ren Yi
Itay Yona
Ilia Shumailov
Aneesh Pappu
Chongyang Shi
Laura Weidinger

Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramatically with access to user information including emails and documents, this raises privacy concerns about assistants sharing inappropriate information with third parties without user supervision. To steer information-sharing assistants to behave in accordance with privacy expectations, we propose to operationalize the design of privacy-conscious assistants that conform with *contextual integrity* (CI), a framework that equates privacy with the appropriate flow of information in a given context. In particular, we design and evaluate a number of strategies to steer assistants' information-sharing actions to be CI compliant. Our evaluation is based on a novel form filling benchmark composed of human annotations of common webform applications, and it reveals that prompting frontier LLMs to perform CI-based reasoning yields strong results.

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NeurIPS Conference 2025 Conference Paper

Privacy Reasoning in Ambiguous Contexts

Ren Yi
Octavian Suciu
Adrian Gascon
Sarah Meiklejohn
Eugene Bagdasarian
Marco Gruteser

We study the ability of language models to reason about appropriate information disclosure - a central aspect of the evolving field of agentic privacy. Whereas previous works have focused on evaluating a model's ability to align with human decisions, we examine the role of ambiguity and missing context on model performance when making information-sharing decisions. We identify context ambiguity as a crucial barrier for high performance in privacy assessments. By designing Camber, a framework for context disambiguation, we show that model-generated decision rationales can reveal ambiguities and that systematically disambiguating context based on these rationales leads to significant accuracy improvements (up to 13. 3% in precision and up to 22. 3% in recall) as well as reductions in prompt sensitivity. Overall, our results indicate that approaches for context disambiguation are a promising way forward to enhance agentic privacy reasoning.

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