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AntTech

Nov 13, 2024 · Artificial Intelligence

Nimbus: Secure and Efficient Two‑Party Inference for Transformers

The article introduces Nimbus, a novel two‑party privacy‑preserving inference framework for Transformer models that accelerates linear‑layer matrix multiplication and activation‑function evaluation through an outer‑product encoding and distribution‑aware polynomial approximation, achieving 2.7‑4.7× speedup over prior work while maintaining model accuracy.

Privacy-Preserving AIcryptographysecret sharing

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Nimbus: Secure and Efficient Two‑Party Inference for Transformers

AntTech

Jul 14, 2023 · Information Security

Open Privacy Computing Protocol SS‑LR: A Secret‑Sharing Based Logistic Regression Framework

The SS‑LR open protocol describes a secret‑sharing based logistic regression algorithm split into four layers—machine learning, secure operators, cryptographic protocol, and network transmission—enabling interoperable, privacy‑preserving data flow and secure multi‑party model training across institutions.

SS-LRdata securitylogistic regression

0 likes · 7 min read

Open Privacy Computing Protocol SS‑LR: A Secret‑Sharing Based Logistic Regression Framework

DataFunTalk

Dec 18, 2020 · Artificial Intelligence

Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions

This article explains the evolution of federated learning, contrasts Google’s cross‑device horizontal approach with China’s cross‑silo vertical implementations, analyzes their security vulnerabilities, and demonstrates how secure multi‑party computation—including differential privacy, secure aggregation, and secret‑sharing techniques—can address these challenges while highlighting performance trade‑offs.

Federated Learningcross-silodifferential privacy

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Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions

DataFunSummit

Dec 16, 2020 · Artificial Intelligence

Federated Learning vs Secure Multi‑Party Computation: Concepts, Challenges, and Alibaba’s Solutions

This article explains the fundamentals of federated learning and secure multi‑party computation, compares their security and performance trade‑offs, discusses the differences between Google’s cross‑device FL and China’s cross‑silo FL, and presents Alibaba’s recent advances and practical solutions for privacy‑preserving collaborative modeling.

Federated Learningcross-silodifferential privacy

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Federated Learning vs Secure Multi‑Party Computation: Concepts, Challenges, and Alibaba’s Solutions