User-Level Differentially Private Synthesis of Question-Answering Data for Multi-Constraint Recommendation

Abstract

In question answering (QA) for multi-constraint recommendation, the model must find every item that satisfies a request with several requirements. Still, training data that pair natural-language questions with complete answer sets are scarce. Such data can be synthesized by computing answers from an item catalog and using users’ ratings and reviews to select requests, yet the result may reveal whose evidence was used. We develop a QA data synthesis framework that protects these users using user-level differential privacy (DP), bounding each user’s total contribution and adding calibrated noise to the support statistics used to weight requests. Using one construction template, we build QA datasets for Movie, Beauty, and Recipe from pre-provided, rule-cleaned, and model-inferred attributes. In ablations without noise, an adversary who knows all other users’ data identifies, without false positives, every tested member whose evidence supports a request, up to 96% of members. Under DP with privacy budget ε=4, it flags at most 3.0% of members of these three datasets at a 1% false-positive rate, and an audit with repeated mechanism runs finds no evidence of privacy loss above ε. Models trained on private data retain at least 97% of the non-private ablation’s macro-averaged F1 on shared test questions.

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