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AntTech
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.

data sharingmulti-party computationtrusted execution environment
0 likes · 17 min read
Shared Intelligence: Combining Trusted Execution Environments and Multi‑Party Computation for Privacy‑Preserving Machine Learning at Ant Group
AntTech
AntTech
Dec 22, 2023 · Information Security

2023 Security and Trustworthy Computing Research Summary – 14 Papers Accepted at Top International Conferences

In late 2023, Ant Group and academic partners reported fourteen security‑focused research papers accepted at top venues such as USENIX Security, ACM CCS, and USENIX ATC, covering privacy‑preserving computation, secure two‑party GBDT training, macOS kernel fuzzing, privacy‑preserving ML frameworks, Rust OOM handling, and more.

MPCSystemscryptography
0 likes · 18 min read
2023 Security and Trustworthy Computing Research Summary – 14 Papers Accepted at Top International Conferences
DataFunSummit
DataFunSummit
Jan 3, 2023 · Artificial Intelligence

Federated Learning Technology Application Innovation Exploration

This presentation reviews the rapid rise of privacy‑preserving computation and federated learning since 2018, explains the fundamentals and classifications of federated learning, and details five technical innovations implemented by China Telecom—including a standard architecture, data‑pollution detection, anti‑member‑inference inference, asynchronous optimization, and contribution‑value assessment—demonstrating practical AI solutions for large‑scale data security and privacy.

Artificial Intelligence
0 likes · 18 min read
Federated Learning Technology Application Innovation Exploration
DataFunTalk
DataFunTalk
Sep 9, 2019 · Artificial Intelligence

Federated Learning: Background, Techniques, Applications, and the FATE Open‑Source Platform

This article presents a comprehensive overview of federated learning, covering its motivation, vertical and horizontal variants, privacy‑preserving technologies, real‑world use cases, and the industrial‑grade open‑source platform FATE that enables secure cross‑organization machine learning.

Data CollaborationFATEfederated learning
0 likes · 16 min read
Federated Learning: Background, Techniques, Applications, and the FATE Open‑Source Platform
Alimama Tech
Alimama Tech
Oct 27, 2021 · Artificial Intelligence

Elastic Federated Learning Solution (EFLS): Architecture, Core Functions, and Technical Details

The Elastic Federated Learning Solution (EFLS) is Alibaba’s open‑source platform that enables privacy‑preserving vertical and horizontal federated learning for large‑scale sparse advertising, offering data‑intersection, high‑performance C++ training, a visual console, novel aggregation algorithms, and a roadmap toward multi‑party scaling and advanced encryption.

AdvertisingElastic Federated LearningFlink
0 likes · 16 min read
Elastic Federated Learning Solution (EFLS): Architecture, Core Functions, and Technical Details
AntTech
AntTech
Aug 17, 2019 · Artificial Intelligence

Shared Machine Learning: Tackling Data Islands with Trusted Execution Environments and Multi‑Party Computation

The article explains how data islands and privacy concerns hinder AI development and describes Ant Financial's shared machine learning approach, which combines Trusted Execution Environments (TEE) and Multi‑Party Computation (MPC) to enable secure, privacy‑preserving data sharing and collaborative model training across organizations.

AIAnt Financialdata sharing
0 likes · 15 min read
Shared Machine Learning: Tackling Data Islands with Trusted Execution Environments and Multi‑Party Computation
Alimama Tech
Alimama Tech
Sep 17, 2025 · Artificial Intelligence

How Federated Learning Balances Privacy and Collaboration in AI

Federated Learning enables multiple parties to collaboratively train a global AI model without sharing raw data, using techniques like local training, encrypted parameter exchange, and secure aggregation, while addressing privacy, communication efficiency, heterogeneity, and incentive challenges across horizontal, vertical, and transfer learning scenarios.

Horizontal FLSecure AggregationVertical FL
0 likes · 24 min read
How Federated Learning Balances Privacy and Collaboration in AI
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Sep 8, 2023 · Blockchain

Can Trustworthy Blockchain Federated Learning Secure AI in Wireless Networks?

This article reviews the background and challenges of data security in wireless communications, introduces Trustworthy Blockchain-based Federated Learning (TBFL), details a two‑layer TBFL architecture with edge computing, discusses its features, key technologies, and autonomous‑driving applications, and outlines current limitations and future research directions.

AI securityAutonomous DrivingBlockchain
0 likes · 18 min read
Can Trustworthy Blockchain Federated Learning Secure AI in Wireless Networks?
AntTech
AntTech
Nov 29, 2024 · Artificial Intelligence

AI Inference with Trusted Execution Environment: HyperGPU and DistMSM Accelerated Zero‑Knowledge Proofs Win 2024 Financial Cipher Cup Innovation Award

The award‑winning solution combines a GPU‑accelerated TEE framework (HyperGPU) and a multi‑GPU zkSNARK acceleration scheme (DistMSM) to provide fast, privacy‑preserving AI inference proofs, earning the third‑place Innovation Team prize at the 2024 Financial Cipher Cup competition.

AIDistMSMFinancial Cipher
0 likes · 6 min read
AI Inference with Trusted Execution Environment: HyperGPU and DistMSM Accelerated Zero‑Knowledge Proofs Win 2024 Financial Cipher Cup Innovation Award
AntTech
AntTech
Jun 2, 2020 · Artificial Intelligence

Privacy-Preserving Machine Learning Workshop at CCS 2020 (Ant Shared Intelligence)

The Ant Shared Intelligence workshop at ACM CCS 2020 invites researchers and practitioners to submit short papers on privacy‑preserving machine learning techniques such as secure multi‑party computation, homomorphic encryption, differential privacy, federated learning, and related applications, with a submission deadline of June 21, 2020.

AI securityCCS2020differential privacy
0 likes · 5 min read
Privacy-Preserving Machine Learning Workshop at CCS 2020 (Ant Shared Intelligence)
AntTech
AntTech
Aug 16, 2023 · Information Security

Ant Group Research Institute Presents Two First-Author Papers at USENIX Security 2023 on Secure MPC for GBDT Training and Efficient 3PC for Binary Circuits

At the 32nd USENIX Security Symposium in Anaheim, Ant Group’s Research Institute sponsored the event and showcased two first‑author papers—one introducing the Squirrel framework for fast, secure two‑party computation of Gradient Boosting Decision Trees, and another proposing an efficient 3‑party protocol for binary circuits in maliciously‑secure DNN inference.

DNN inferenceMPCUSENIX Security
0 likes · 3 min read
Ant Group Research Institute Presents Two First-Author Papers at USENIX Security 2023 on Secure MPC for GBDT Training and Efficient 3PC for Binary Circuits
AntTech
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
vivo Internet Technology
vivo Internet Technology
Aug 23, 2023 · Artificial Intelligence

Federated Learning: Privacy-Preserving Collaborative AI Across Data Islands

Federated learning enables multiple organizations to jointly train high‑performing AI models without sharing raw data, using techniques such as secure multi‑party computation, differential privacy, and homomorphic encryption, thereby overcoming data‑island and regulatory constraints and supporting applications in mobile edge AI, finance, retail, and healthcare.

Data IslandHomomorphic Encryptiondifferential privacy
0 likes · 19 min read
Federated Learning: Privacy-Preserving Collaborative AI Across Data Islands
DataFunTalk
DataFunTalk
Nov 17, 2024 · Artificial Intelligence

Federated Learning and Data Security in the Era of Large Models: Research Overview and the FLAIR Platform

This presentation reviews recent research on data security and utilization in the large‑model era, covering privacy‑preserving federated learning, knowledge‑transfer techniques, prototype‑based modeling, multi‑model fusion methods such as FuseGen, and introduces the federated knowledge computing platform FLAIR for both horizontal and vertical federated scenarios.

Data SecurityFLAIRKnowledge Transfer
0 likes · 19 min read
Federated Learning and Data Security in the Era of Large Models: Research Overview and the FLAIR Platform
DataFunSummit
DataFunSummit
Oct 23, 2021 · Artificial Intelligence

Privacy Computing: The Federated Learning Three‑Part FIRM Architecture and Its Industrial Applications

This article introduces the background of privacy computing, explains the three‑stage FIRM reference architecture for federated learning, describes key technologies such as the Ionic Bond communication framework and HeteroDeepFM, and showcases real‑world applications in marketing, risk control, and government sectors.

AI securityData CollaborationFIRM architecture
0 likes · 17 min read
Privacy Computing: The Federated Learning Three‑Part FIRM Architecture and Its Industrial Applications
Alimama Tech
Alimama Tech
Sep 3, 2025 · Artificial Intelligence

Privacy-Preserving Machine Learning: Balancing Data Utility and Confidentiality

Privacy-Preserving Machine Learning (PPML) integrates cryptographic techniques such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation to enable model training and inference on encrypted or distributed data, thereby breaking data silos while safeguarding privacy across sectors like healthcare, finance, and advertising.

Homomorphic EncryptionMachine Learningfederated learning
0 likes · 18 min read
Privacy-Preserving Machine Learning: Balancing Data Utility and Confidentiality
AntTech
AntTech
Jan 6, 2025 · Artificial Intelligence

2024 Security and Trusted AI Research Highlights from Alibaba, Tsinghua, Zhejiang, and Partner Institutions

This article presents sixteen peer‑reviewed research papers published in top conferences and journals in 2024, covering trusted AI, large‑model applications, network security, adversarial training, deep‑fake detection, secure inference, and related topics from collaborations among Alibaba, Tsinghua, Zhejiang, and other leading institutions.

AI securitySecure InferenceTrusted AI
0 likes · 27 min read
2024 Security and Trusted AI Research Highlights from Alibaba, Tsinghua, Zhejiang, and Partner Institutions
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Aug 15, 2022 · Artificial Intelligence

Federated Learning Elevates Mobile Network Intelligence: Architecture & Demo

This article reviews the evolution of federated learning, outlines its algorithms and standards, proposes centralized and decentralized network‑intelligence architectures for mobile communications, and presents a customer‑experience‑management case study that demonstrates how federated learning improves model accuracy and privacy across multiple regional nodes.

AIMobile NetworksNetwork Intelligence
0 likes · 22 min read
Federated Learning Elevates Mobile Network Intelligence: Architecture & Demo