Learning-theoretic protocol lets two companies privately test data compatibility before pooling
roydanroy · x · 2026-11-06
Mahdi Haghifam et al. propose a two-party differential privacy protocol that lets two companies jointly assess whether their private user datasets are "compatible" for training a shared model—without revealing raw data. The paper frames the problem learning-theoretically: if a shared hypothesis fits both datasets, collaborate; if their policies conflict, don't pool. Illustrated with a spam-classification example.
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