privacy-preserving ML

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 LearningGraph Neural Networksadversarial learning

0 likes · 20 min read

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

AntTech

Aug 18, 2020 · Artificial Intelligence

Shared Intelligence vs. Federated Learning: Techniques, Challenges, and Ant Group’s Practical Experience

The article compares shared intelligence and federated learning, examines privacy‑preserving techniques such as MPC, TEE, and differential privacy, discusses gradient‑inversion attacks and their mitigations, and presents Ant Group’s end‑to‑end system design and real‑world deployments in finance.

AI securityAnt GroupFederated Learning

0 likes · 22 min read

Shared Intelligence vs. Federated Learning: Techniques, Challenges, and Ant Group’s Practical Experience

AntTech

Jun 17, 2020 · Artificial Intelligence

Shared Intelligence: Combining Trusted Execution Environments and Multi‑Party Computation for Privacy‑Preserving Machine Learning at Ant Group

This article presents Ant Group's shared‑intelligence solution that integrates Trusted Execution Environments (TEE) and Multi‑Party Computation (MPC) to enable privacy‑preserving data sharing and large‑scale machine‑learning across untrusted parties, discusses industry progress, technical evolution, practical deployments, and future challenges.

Ant GroupData Sharingmulti-party computation

0 likes · 17 min read

Shared Intelligence: Combining Trusted Execution Environments and Multi‑Party Computation for Privacy‑Preserving Machine Learning at Ant Group

AntTech

Mar 4, 2020 · Artificial Intelligence

Shared Intelligence vs. Federated Learning: Ant Group’s Privacy‑Preserving Machine Learning Solutions for Finance

The article explains how Ant Group tackles the privacy‑usability trade‑off in AI by combining Trusted Execution Environments and Multi‑Party Computation into a “shared intelligence” framework, contrasting it with federated learning, detailing technical architectures, training workflows, and its impact on financial data sharing.

Data SharingFederated Learningfinancial technology

0 likes · 14 min read

Shared Intelligence vs. Federated Learning: Ant Group’s Privacy‑Preserving Machine Learning Solutions for Finance