Researchers have developed a new method for training Gradient Boosting Decision Trees (GBDTs) on vertically partitioned data while preserving the anonymity of record identifiers. This approach addresses the security vulnerabilities of existing methods that rely on Private Set Intersection (PSI), which can inadvertently expose shared IDs. The proposed protocol uses a dual circuit-PSI design and oblivious programmable pseudorandom functions to enable secure, ID-hiding aggregation, offering a more efficient and private solution for sensitive data analytics in fields like finance and healthcare. AI
RANK_REASON This is a research paper detailing a new technical approach to secure machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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