Google Research has developed a new parallel algorithm for differentially private partition selection, enabling the secure release of large datasets for AI and machine learning. This method adds controlled noise to data selections, ensuring individual user privacy while still allowing for the identification of common items across vast collections. The algorithm scales to datasets with hundreds of billions of items, significantly outperforming previous sequential methods and offering improved utility without compromising privacy. Google is open-sourcing this technology to foster community innovation. AI
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RANK_REASON Publication of a novel algorithm in a research paper with open-source code release.
