How Can We Achieve Efficient Privacy-Preserving Machine Learning? A Cross‑Layer Review

This systematic review, authored by Peking University and Ant Group researchers, examines efficient privacy‑preserving machine learning (PPML) across protocol, model, and system layers, identifies key optimization challenges, proposes a unified cross‑layer perspective, and outlines future research directions for large‑model privacy and performance.

Cross-Layer OptimizationEfficient MLMachine Learning Security

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How Can We Achieve Efficient Privacy-Preserving Machine Learning? A Cross‑Layer Review

AntTech

Mar 31, 2022 · Artificial Intelligence

Trustworthy AI in the Digital Economy: Practices and Explorations by Ant Group

In a keynote at the Machine Heart AI Technology Conference, Ant Group's Zhou Jun presented the concept of trustworthy AI, detailing its integration with privacy, security, graph learning, explainable and adversarial machine learning, and large‑scale privacy‑preserving techniques to enhance financial risk control in the digital economy.

Explainable Machine LearningPrivacy-Preserving MLadversarial learning

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Trustworthy AI in the Digital Economy: Practices and Explorations by Ant Group